{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第7章 数据的模型分析及可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1 线性相关分析模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 　7.1.1  线性相关的概念和模拟"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7.1.1.1 线性相关的概念"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7.1.1.2 线性相关的模拟"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-4.    , -3.5789, -3.1579, -2.7368, -2.3158, -1.8947, -1.4737,\n",
       "       -1.0526, -0.6316, -0.2105,  0.2105,  0.6316,  1.0526,  1.4737,\n",
       "        1.8947,  2.3158,  2.7368,  3.1579,  3.5789,  4.    ])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.set_printoptions(precision=4)             #设置numpy输出精度\n",
    "x=np.linspace(-4,4,20)            #构建[-4,4]上 x 的数据向量 \n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.6243, -0.6118, -0.5282, -1.073 ,  0.8654, -2.3015,  1.7448,\n",
       "       -0.7612,  0.319 , -0.2494,  1.4621, -2.0601, -0.3224, -0.3841,\n",
       "        1.1338, -1.0999, -0.1724, -0.8779,  0.0422,  0.5828])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)                 #设置随机种子数以便重复结果\n",
    "e=np.random.randn(20)             #随机误差数据向量 e~N(0,1)\n",
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "y1=x; plt.plot(x,y1,'o');         #完全正相关 y=x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y2=-x; plt.plot(x,y2,'o');        #完全负相关 y=-x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y3=x+e; plt.plot(x,y3,'o');       #正相关 y=x+e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y4=-x+e; plt.plot(x,y4,'o');      #负相关 y=-x+e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 　7.1.2 相关系数的计算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7.1.2.1 相关系数的计算公式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7.1.2.2 相关系数的直观分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(2,2,figsize=(10,8)) #将上述四个图放到一页上\n",
    "ax[0,0].plot(x,y1,'o'); ax[0,1].plot(x,y2,'o')     \n",
    "ax[1,0].plot(x,y3,'o'); ax[1,1].plot(x,y4,'o');   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7.1.2.3 相关系数的Python计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y1,'o');\n",
    "np.corrcoef([x,y1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1., -1.],\n",
       "       [-1.,  1.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y2,'o');\n",
    "np.corrcoef([x,y2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.907917278678589"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y3,'o');\n",
    "np.corrcoef([x,y3])[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9140292045707705"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/ZvurEfHJFI0X17WS86HPtm9a8fEeSS83VctKtndp6Z90d0fEL5uuJ1PPSrrJ9hbb10u6V9LjDdeULS+9RX1Z0vmI+FzT9SyzPbU8K8v2hKSPK4O/hxFxICI2dTLrXkmnUoW4VGCQZ+6Q7Rdtv6Clrp8spmRJ+oKkGySd7EyN/FLTBUmS7T+yfVHSRyU9aftEU7V0BoPvl3RCSwN3/x4RLzVVzzLb/yrpvyRttX3R9l80XVPHDkmfknRH5/+pM523zab9hqSnO38Hn9VSH3nSqX45YmUnABSON3IAKBxBDgCFI8gBoHAEOQAUjiAHgMIR5ABQOIIcAApHkANA4f4ft6ajGJiabLYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y4,'o');\n",
    "np.corrcoef(x,y4)[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.    ,  1.    , -1.    ,  0.9079, -0.914 ],\n",
       "       [ 1.    ,  1.    , -1.    ,  0.9079, -0.914 ],\n",
       "       [-1.    , -1.    ,  1.    , -0.9079,  0.914 ],\n",
       "       [ 0.9079,  0.9079, -0.9079,  1.    , -0.6598],\n",
       "       [-0.914 , -0.914 ,  0.914 , -0.6598,  1.    ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef([x,y1,y2,y3,y4])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y1</th>\n",
       "      <th>y2</th>\n",
       "      <th>y3</th>\n",
       "      <th>y4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4.000000</td>\n",
       "      <td>-4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-2.375655</td>\n",
       "      <td>5.624345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.578947</td>\n",
       "      <td>-3.578947</td>\n",
       "      <td>3.578947</td>\n",
       "      <td>-4.190704</td>\n",
       "      <td>2.967191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-3.157895</td>\n",
       "      <td>-3.157895</td>\n",
       "      <td>3.157895</td>\n",
       "      <td>-3.686066</td>\n",
       "      <td>2.629723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.736842</td>\n",
       "      <td>-2.736842</td>\n",
       "      <td>2.736842</td>\n",
       "      <td>-3.809811</td>\n",
       "      <td>1.663873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.315789</td>\n",
       "      <td>-2.315789</td>\n",
       "      <td>2.315789</td>\n",
       "      <td>-1.450382</td>\n",
       "      <td>3.181197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.315789</td>\n",
       "      <td>2.315789</td>\n",
       "      <td>-2.315789</td>\n",
       "      <td>1.215898</td>\n",
       "      <td>-3.415681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.736842</td>\n",
       "      <td>2.736842</td>\n",
       "      <td>-2.736842</td>\n",
       "      <td>2.564414</td>\n",
       "      <td>-2.909270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.157895</td>\n",
       "      <td>3.157895</td>\n",
       "      <td>-3.157895</td>\n",
       "      <td>2.280036</td>\n",
       "      <td>-4.035753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.578947</td>\n",
       "      <td>3.578947</td>\n",
       "      <td>-3.578947</td>\n",
       "      <td>3.621161</td>\n",
       "      <td>-3.536734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-4.000000</td>\n",
       "      <td>4.582815</td>\n",
       "      <td>-3.417185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           x        y1        y2        y3        y4\n",
       "0  -4.000000 -4.000000  4.000000 -2.375655  5.624345\n",
       "1  -3.578947 -3.578947  3.578947 -4.190704  2.967191\n",
       "2  -3.157895 -3.157895  3.157895 -3.686066  2.629723\n",
       "3  -2.736842 -2.736842  2.736842 -3.809811  1.663873\n",
       "4  -2.315789 -2.315789  2.315789 -1.450382  3.181197\n",
       "..       ...       ...       ...       ...       ...\n",
       "15  2.315789  2.315789 -2.315789  1.215898 -3.415681\n",
       "16  2.736842  2.736842 -2.736842  2.564414 -2.909270\n",
       "17  3.157895  3.157895 -3.157895  2.280036 -4.035753\n",
       "18  3.578947  3.578947 -3.578947  3.621161 -3.536734\n",
       "19  4.000000  4.000000 -4.000000  4.582815 -3.417185\n",
       "\n",
       "[20 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd   #构建模拟数据的数据框\n",
    "pd.set_option('display.max_rows', 10)\n",
    "xy=pd.DataFrame({'x':x,'y1':y1,'y2':y2,'y3':y3,'y4':y4});xy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x     1.000000\n",
       "y1    1.000000\n",
       "y2   -1.000000\n",
       "y3    0.907917\n",
       "y4   -0.914029\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xy.corrwith(xy.x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y1</th>\n",
       "      <th>y2</th>\n",
       "      <th>y3</th>\n",
       "      <th>y4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.907917</td>\n",
       "      <td>-0.914029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.907917</td>\n",
       "      <td>-0.914029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y2</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.907917</td>\n",
       "      <td>0.914029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y3</th>\n",
       "      <td>0.907917</td>\n",
       "      <td>0.907917</td>\n",
       "      <td>-0.907917</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.659836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y4</th>\n",
       "      <td>-0.914029</td>\n",
       "      <td>-0.914029</td>\n",
       "      <td>0.914029</td>\n",
       "      <td>-0.659836</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x        y1        y2        y3        y4\n",
       "x   1.000000  1.000000 -1.000000  0.907917 -0.914029\n",
       "y1  1.000000  1.000000 -1.000000  0.907917 -0.914029\n",
       "y2 -1.000000 -1.000000  1.000000 -0.907917  0.914029\n",
       "y3  0.907917  0.907917 -0.907917  1.000000 -0.659836\n",
       "y4 -0.914029 -0.914029  0.914029 -0.659836  1.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xy.corr()   #根据数据框计算相关系数矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+9jLiE2LJDnbV4rRaqCixyxRMkyp32XlkRzMDUyF2ymJeWbW6rpT7NjYQiMbpXlVjdDhFzWpRfHhXK5fHAmxpKa7aTnNZTtLyOa31kfTvQ8CHlFKfXOqLaK1/d9ZNX0rf/jZL724SYkmSSY3lhnVXXHYrh9bWGRiRWIw19WWsmaPgXCKpZR2dDLteAyqZzPyYFrE0bdVu2qoXdzE1+7Ot0Cw5abkhYbnxtn/MTDhCZN+lUR8/ODVMldvOx7rbcdkXX6FVmEsyqfn28QH6poLcta6OPdIqkFGnB6b56dlRGiqcPLanDbss/W9akXiCrx/pZzIQ5Ve2NLGhqTALOMoRKIrOhRE/iaRmwh9lRAbd5jV/NE7vZBCt4cyQz+hwCs75YR9JrRmeDjMViBodjljAmC/CmC9CIqk5N1y4w0ElaRFFZ1trJeUuGx01blqqSowOR6xAudPG5pYKSp1Wdss4l4zb2VFFmdPG2oaygpw+W0iaKlysriul3GVjZ7prrxDJAhSi6LTXuPmNu7qMDkNkgFKKX5Fii1kz3xgiYT42q4UP72o1Ooysk5YWUdCmgzF+dHqII9cmjQ5F5NDZIS8/ODXE8HRxd/+dGUy9D9INWjgm/BF+eGqIk30eo0MxhLS0iIL2y0vjXBjxAT7aqt0z9YRE4QrHEvz4nWG0Tn3Af/Jgp9EhGSIUTfCTM6n3YTIQ5RMHVhkdksiAl86P0TcZ5Nywj1W1bqrchVPBeTGkpUUUtOuF2VL1g2SWUDGwWy2Uu1L7vbq0uD7Qb2S3KsrSJShqivh9KDQ1palju8RhLcqZj9LSIgrawTW1tNe4qSixz3yRicJmtSie2NfOmC9CaxEPtLZZLTy5v4MxX2TRa3wI87tnfQNr68upKXMUZdIiLS2iYCSTmmvjAaaDsZnblFK017ipLJGEpZi4HTZW1ZZiu2FdkWA0zpUxf1EVW7z+PmRj4b1RX5j+qWDGX1cszGJRdNS6Z1rRciGR1FwdD+ANx27/4CyTlhZRMH5xcYzjvR4cNgufPtQp1ZnFjGRS8+zhPqZDMVbVunl0d5vRIeW1QU+Irx3pQ2t4cHMjW1srjQ5JZNFPz47wzqCXEoeVTx/qNLSFR1paRMGYDqWuAqLxJCEpqCdukNQafyQOgDdk/NVivvOF4zMFFafl/Sx41/dxOJYgmjC2pVIuRUXBuGd9AyX2CRorXLIQlriJzWrh/duauTTqZ0e7tAqs1LqGMvZ31RCJJdmzqtrocESW3b+pkbeuTdJWXUKFwWMDJWkRBaPSbee9stCYmMfahjLWNshCaZlgsSgOrZECo8WiptRhmkUcJWkReen8sI+fnRulpcrFB7a3FHRVU5E9L58f5cyQlz0d1ezvqs3Zdl88M8KFUR/7V9dKS4W4xbXxAD9+Z5iaUgcf3tUqhSpvIO+EyEsn+zyEYwmujAWYDEohN7F0yaTmeK+HSCzJ8RyuLqo1nBqYJhJLcqJIVzUVCzs1ME0wmqB/KlT0qzrPJkmLyEubWyqwWhRt1SVUF9mKkCIzLBbFlpYKLEqxtSV341yUgg1N5VhUavtCzLapuQK7VdFQ4aShQsbn3Ui6h0Re2tpayZaWCpSSbiGxfO/d0sSDmxtzfhy9f1szequW41fMaW1DGb9971o5PuYgLS3C9DzBKC+dG+XSqO+m2+WEFpkw+zga8IR46dwoQ9OhnG43Fy6N+njp3Cge6VI1PTN9vl0c8fHS+dGbFu40Sk6TFqXUB5RSbyilXldK/f4c9/+9UupNpdTLSqkncxmbMK8Xzoxwos/D828Pz6y1IUS2fO/EICf6PDx3ctDoUDLKH4nz/beHONHn4YUzI0aHI/KELxzj+VNDnOj18OJZ44+bXHcPnQTuAJLAy0qp/6m1np71mF/TWl/KcVzCxK6vbGu3KWwyS0hkWanTSjiWoMRRWL3nNovCYbMQiSVltWixaHarBbvVQjSexO0wvtZRTo9crXXv9d+VUnFSyctNDwH+QSk1AfxzrXVPLuMT5vTg5kbW1JfRUO4sygJhIrc+uruNvqkgHTWFVWTQZbfyxN4ORn0RuupLjQ5H5AmX3cqT+8xz3BiSbiul3gdc1lr7Zt31+1rrSaXUncBfAI/N8/yngKcAOjo6shqryK1kUjPiC1PtfreCqd1qYUNTucGRiWJR6rSxsenmWT3xRJIxf4S6MmdW1syY67jPhupSB9WlMtuuGIWiCaZDMRornEseL2Om4yYnSYtS6l8BDwN/D/wC+EPgkdmP01pPpn/+Uin1p/O9ntb6aeBpgO7ubp2FkIVBXkwX5qpy2/nkgVU3VekVwijfPTFI72SQ5koXH9+X+Quln5wZ4eyQl2q3nU8e7MxKVWZRvMKxBP/0Rg/+SJw9q6q5e3290SEtW06+EbTWf661vgf4JqnE5XNa68DsxymlKtI/NwCeXMQmzGXUFwHAE4wZXphLiOvG/KnjcswXQevMXyeN+VILiHlCMaJxOe5FZgWjiZlJDNc/Y/NVrruH/jmwGvi7dPPUZ4AQqSTmS8CXlVLVpMa2fCHHsQkTuHdjA29dnaSzrhR3gQ2EFPnrgU2NnB6YZlNzdtYGundjA0euTbG6rpQSEwx2FIWlptTBnevqGPSEOLgmd+UqsiHXA3H/E/Cf5rjrS+n7P5DLeIT5tFaV0Lqr1egwhLhJtosttlW7aasurIG/wlz2dtYYHUJGyKWsMMyZQS9vXp1gXUM5d66TirEiv5zqn+ZIzyQbmyoycvU6HYrxg1NDWJXi4e3NMi1Z5NyoL8yP3xmh3Gnj/duacdjMN6bQfBGJovHGlQk8wRhvXZskHEsYHY4QS/L6lXE8wRhvXJkgkVz5OJezQ16Gp8MMeEJcGJk9sVKI7DvZN824L8LV8QC9k7cMOzUFSVqEYa43t7fXuHGaMKMXYiHXj9+u+tKMzPZZVevGYbPgtFukq0gYYnVd6lguc9porHAZHc6cpP1RGObu9fXsW12D02YxVZ0NIRbjvo2NHFpTl7GEu7myhKfu7gLIylowQtzO2oYyPv+eLmwWi2mn3cuZIXLCE4zy+uUJRrzhm2532a2SsIi8Nfv4nQ7GeP3yxG2LLY76wrx+eYIJ/83TT68vmS6EUZw260zCcmnUx+Grk0Ti5um+l7ND5MRzJwd548oE3zzWTzID/f9CmNHzp4Z448oE3zo2QGyBdYa+fWyAN65M8N0ThVWUURSOUW+Y504O8eqlcV69NG50ODMkaRE5YUln7lZpVREF7HojiUUpFjrSr1/J2qxyPghzUkpx/ePaYqLPbRnTInLigztauDTqZ1Vt6UwCI0SheWR7CxdGfLTXuBcsQfHYnjaujgfoqs/e2i9CrER9uZOP7m5jKhhlc3PF7Z+QI5K0iIxLJjXecIwKl30mQSl32dnVUW1wZEJkV6nTNudx7glGKXPaZhKZKreDXR2ZK0AXjScJxRJUltgz9ppCtNe4ac9BtfO5vjPmI0mLyLgfnh7mwoiPjho3H93TZnQ4QhjqpfOjnOj10FDh5Im9mS+2eL0Yni8c5+719exZJRcHIr98/9QQl0f9rK4r5cO3WRFdxrSIjOudDALQPxXKSnE5IfJJf/p8GPVGCGdhFoYnGMMXThXD60tvS4h8cv24XczxK0mLyLj3rK+nqdLFPRvqZTqzKHp3rK2jqdLFwTW1WSkC2ljhZGd7FS1VLg505XcxPFGc3v3OaLjtY1W+XwnX1dXpzs5Oo8MQadeuXUP2h7nIPjEX2R/mIvvDfI4ePaq11nM2quT9mJbOzk6OHDlidBhF69ywl5+8M0JDuZOP7mnj4P59sj9MZufuPfzGX3wVq1I8tqeN6tLMDQAVS9fd3S3niIGmQzG+fqSPWELzkV2tPHL/nfPujzFfhG8d68eiFB/d00aNnDs5oZQ6Nt990j0kVuTskJdEUjM0HWbCHzU6HDGHSCxBKJrAH4lzdcKcRdCEyJXeiSC+cJxwLMGlUf+Cj7085id4/dwZl3PHDPK+pUUYa3tbFaPeCA0VTurK5CrEjJx2K1VuO1aLYo2sCyKK3Or6Uur6nUTjSTY0lS/42PWN5Zwb8mKxqJkCmcJYkrSIFVlTX8aa98jJbGY2i+Izd6w2OgwhTKHMaeOTB1Yt6rE1pQ4+LeeOqUjSIhZtKhDl8LVJWipL2NZWedvHd37x+RxEJeZy7U8fXvD+eCLJa5cn0MChNbVSpE/kvaHpECf7PKypL2Nd48ItKIvhC6eKX9aWOdizqiYDEYpMkKRFLNrLF0a5Nh7kzKCXtuoSGdCZx04NTHO0ZwqAcpeN3bJaschzP3lnhMlAlPPDfr5QW4rDtrJE/NVL45wd8gHQXFlCS1VJJsIUKySXV2LRri8R7rJbcdmtBkcjVuLG5d5l6XdRCK4fx2UuG7YM1DerSL+e3aoozcL6OmJ5ZE+IRbtnfQNddWXUlDkocUjSks+66st4cn8HWkNTpcvocIRYsYe3N9M/FaKxwpmRoqwHu2ppq3JTUWKj0i2JvVlI0iLmlExq+qaC1JY5KXOmDhOLRdFZV2pwZCJTGituTVZGfWGSSUlkhHFGvGGUgobypR2DdquF1Sv4fOqfClLqsM10eyul6KjNfrFAsTSStIg5/fTcKKcHpnE7rHz6jk6cNmlZKXR9k0G+eawfrVNXreszMJhRiKW4NOrjuZNDKAUf2dXKqtrcXCQd653i5+fHsFoUT+7voK7MmZPtiqWTMS1iTlPB1EJxwWiCSDxpcDQiFzzBGNereniCMWODEUXp+nGndW6PQU/68y6R1DPFJ4U5SUuLmNN9Gxt46+ok7TVuKlzSn1sMNjWXMxWMkkhqdrTffkq7EJm2va0KXySORSm2tFTkbLv7V9cST2jKXXY6pUvI1CRpEXOqK3Pyvm3NRochcshmtXD3+nqjwxBFzGGzcO8iKv1mWqnTxnu3NOV8u2LpJGkRnB6Y5tVL46yqLeVXtjSi1MpH3ovCEIjE+c6JAaLxJI9sb6G+XPr6RW6Y9dibDsb43skBNPChHa0ysyjHDBnTopTar5R6TSn1S6XUX826798ppU4qpV5WSv2eEfEVm+N9HoLRBGeHvPgj0p8r3nV1PMCoN4InGOPcsNfocEQRMeuxd3HUx7g/yoQ/yoVRn9HhFB2jBuL2APdpre8EGpRS22bd//ta63u01n9pQGxFZ3NzOUrBqlq3LKIkbtJe46bcZcNhs0ixRZFTZj32OutKcTuslDisK5piLZbHkG8orfXwDX/GgMSsh/xnpdQU8Ada6xM5C6xI7VlVw6726owsyCQKS2WJnc/dmSoYJ92GIpfMeuzVlTl56u4uwFxxFQtDpzwrpbYD9VrrMzfc/Nda6z3AF4D/Ns/znlJKHVFKHRkbG8tFqAVjMhDl5fOj9EwEbrpdEhYxH6XULR/Ox3uneO3SOFGZDi9u49p4gJfPjzIZiC75uXMde7mUSGoOX53krWuTJJPaNHEVM8P6ApRSNcDfAB+78Xat9WT658X5Dgqt9dPA0wDd3d16zgeJOf3w9BCj3gin+qd56j1dsmicWLLLY35ePp+6WNDAHWvrjA1ImFYknuB7JwdJJDWDnjBP7u8wOqQlOZWepADgtFnY3lZlbEDCsIG4NuCfSHX/DM+6ryL9sw6Z3ZRxJelChw6bBatcKYhlcNmtXD90pHCmWIhFqZlqyyWO/FvL1GV/N+YSOdZNwaik4HFgL/Bn6daUPwKe1Fr/DvDnSqmtpBKqLxoUX8F6/7ZmrowFaKlyYbPm34eIMF5rVQmP7WkjHEuYaoCkMB+71cLH97Yz6AnTVZ9/g1Y3NlXgtFlRIHXXTMKogbjPAM/Muvn19H2fz31EhSmZ1Iz5I1S7HTNXOy67lc05XGlSFKa26ltXDfVH4kRiCWqlbou4QZXbQZXbkbHXy/VxlqsZQuFYAm84tuRCkcVGul8K2A9PD3NhxEd9uZNf298hA8dE1kwFonzlcC/ReJIHNzeytVXKAIjMmwxEeSZ9nL13SyNbWgrjOAvHEvzj6z34I3H2ra6RcWILkP6BAjbsDQMw7o8QT8p4ZZE9k8HozEyikfRxJ0SmTQYiBXmc+SPxmYU9h6cL5/+VDdLSUsDu29jAsZ4pNjSVY5fxKyKLVteWsrO9Cm84RndnjdHhiAK1uq6MHe2V+COJgjrO6sqcHOiqZdgb4o410sqyEElaCtjqulJZsVHkhMWiuHdj7gvdieJitSju29hodBhZcXBNrdEh5AVJWgrE2/0ejvd62NxSwd4CugIR+SueSPKjd4bxBGM8uLmRxgoZYFhM/JE4Pzw1hCY1a7HMWXxfN69fnuDCiI+9nTUyASJDpM+gQLx2eYLJQJRXL43ftHKjEEbpmwpxccTPmC/C8d4po8MROXZuyEv/VIiBqRBnh8xT8DBXovEkb1xJfS6/dnnc6HAKhiQtBaIr3Q20uq5UluQXptBQ7qTcZUMpWeOiGLXXuHHYLDhsFtrnmCJf6OxWRXtN6v8t6xllTvG11xWo925p4o61dbgdsmqjMIdSp41PH+okntSycm4Raqxw8c/u6kKji7JciFKKj+5uJRhNUFqEXWPZIi0teWgyEOXNKxNM+CM33V7qtMlaLMJUbFbLLQnLlTE/R65NEonPLu4u8t3ZIS/He6dIpLuoHTZLUSYs1ymlbklYLo/5OdozKcVGl0nSvzz0rWP9+MJxTg1M8xt3dRkdjhCLNuaL8L2Tg2gNnmCMBzYX5kyQYnRlzM+PTqdKycUSmn2rZULAbKPeMM+lj//pUKxgZ0Jlk7S05CGLtKaIPHXjoWuRT5+CcmMrrwyrm9uN75G0ii+PtLTkoUd3t3J5zE9XnQzuEvmlrszJo7vamAhEZKn/ArO6rpRHtjcTjiXZItN751Rf7uQju1qZCsbkPVomSVpMLpnU+CJxKlzvjlepcjvYs0qaXkV+6qh101F782ySWCJJOJag3GU3KKrioLXGG45T7rRlZZbhusbyjL+mmUXjSWKJ5JIG2q6qLWWVrCO3bJK0mNz3Tg5ydTzAhqZy3r+t2ehwhMi4cCzBl9/sxRuKcc+GenZ1VBsdUsF64cwI7wx6aasu4fHudqPDyWu+cIyvvNlLKJbgoa1NbGySlpNckF5lE9Na0zMRBODaRMDgaITIDk8whjcUA5g53kV29E6m3t/+qRDxhMxeWYlRX4RgNIHWctzmkiQtJqaU4u71ddSXO7l7Xb3R4QiRFY0VTra3VdJY4ZIZJ1l257rU58ld6+qwSRHVFVlV42ZjUzktVS72rJLWwVyR7iGT29VRLc3loqAppbh/k0z9zIWNTRXSjZEhNquF90mXfc5Jqm0iJ/s8fO2tPi6N+owORQjDvXppnK8f6WNoOmR0KCLD3ro2ydeO9NEr3SpZobXmpXOjfONoP+OzFiHNd8tKWpRSD650w0qpv1JKvaKU+q+zbt+qlPqlUupVpdT2lW4nX8QTSV46P8qAJ8TL58eMDkcIQ437Ixy+Okn/VIjXLk0YHY7IIH8kzi8vjjMwFeIXF+WzLhv6p0Kc6PPQNxnkzSuTRoeTUcttafnblWxUKbUbKNNa3wU4lFJ7b7j7PwBPAB9L/14UbFYLTRUuAFqqSgyORghjlTltlLtSvddyPhSWEruV2jIHAK2yb7OiptQxU4eupcplcDSZNe+YFqXU9+a7C1jpLPMDwAvp318EDgJvpf+u1lr3pWOoWuF28spje9rwhGLUuB1GhyKEoVx2K588uIpAJEFNqZwPhcRqUTyxrwNvKCb7NktKnTY+daiTUDRBdYG9xwsNxL0L+ATgn3W7AvatcLtVwJX079PAlhvuu7H1Z87Vj5RSTwFPAXR0dKwwFGNMBaL0T4VY21BGSTojtlkt1JU5DY5MCHNw2qy3FNsb9YUZmY6wvqmsqAvx5ULvRJBANM6GxvKML0Rnt1qolc+6RfMEo/ROBlnbUIbbsbj5My67tSCrqy/0v38DCGqtfz77DqXU+RVudxq4PoS9AvDccJ++4fc5FxLQWj8NPA3Q3d2t53qMmcUTSb56pI9QNMHZYS8fk0WehLitUDTB14/0E40n6ZkM8Mj2FqNDKlgDnhDfPNYPgC8cl6noBkomNV870kcgkuDMoJeP78vPC/VMmXdMi9b6fVrrl5RSv6OUqp51390r3O7rwP3p3x8glSBdN6mUalNKtQDeFW7HlDTMLOwUkwWehFiUhNbEE6lrFDlvsisWf/f9lffaWJpU1WyAqOyLRa3T0gi8pZQ6Bvwd8GOt9YpaN7TWx5RSYaXUK8AJoFcp9a+11l8C/gT4avqhv72S7ZiV3Wrhw7tauTYelKJZQixSmdPGB3e2MOgJsb1Nii1mU2ddKQ9ubiQQibNbFk4zlNWi+MiuVq6MBdgs3xe3T1q01n+slPo3wHuBzwB/o5T6GvC3WuvLy92w1vp3Z930pfTtbwN3LPd1zSqWSGK/YQXKtmo3bdXuBZ4hhJhtdV0pq+tKb7l99vklVu56Fe5YIonWeqZgq8i9lqqSRc2i01oTT+qCPhcWNaJHa62VUsPAMBAHqoFvKKVe0Fr/YTYDzHfJpOZbxwfomwxycE0tB7qkvKcQmZJMar55rJ/+qRB3rK2TsRcZdqp/mp+eG6G+3MnHutsL+ssw30XiCb76Vh9TgRjv3dLIpubCbJW57RGolPpdpdRR4M+AV4FtWusvAHuAj2Y5vrwXjCXoSxcpuzAiK90KkUn+aJz+qdSKuefl/Mq4CyM+tIZRb4SpQNTocMQCxnwRJvxRkloX9HfNYlpaaoBHtdY9N96otU4qpR7JTliFo8xpY0d7JVfGAnSvkqtAITKp3GljW2sl1yYC7O2UsReZtntVNZ5QjOZKlyzHYHLNlSWsaShj3BdhdwHXq1vMmJY/WeC+s5kNpzDdt7GR+zYaHYUQhUcpxQObpdhitqyuK+Vzd642OgyxCFaL4oM7Cn8ZAOmgzLCTfR6eOznIqDdsdChCFK13Bqd57uQgg57iLrZ4eiD1PkjRycIx5ovw/bcHOdY7ZXQohpCkJYN84Rg/OzfKpVE/L50fNTocIYpSOJbghTMjXBr189OzI0aHY5hQNMGLZ6+/D/J5VCh+fmGMiyN+fn5+DE+w+MYZSdKSQU6bdabIW22p9P8KYQS71UKFyw5Q1EvF262K8vT7UFdWWPVnitn1YpOlzsJcpv92FlfEQCyKw2bhyf0dTAaitFRK9VIhjGC1KJ7c38GYL1LUFaJtVgtP7utg3B+RasoF5J719axrKKPa7SjKpEVaWlbAE4xyadRPIvnuAsFuh422anfGC4wJIRbPZbfSXuPGesN56I/EuTTqIxJPGBhZbpU4Uu9DNj6PRrxheieCGX9dsTClFG3VbkqduWtziCeSXBr1Mx2M5Wyb85GWlmUKRRN85XAvkViSra2VPCgzGIQwrWRS8+zhXnzhOB01bj66p83okPLagCfE14/0oTU8sKmRbVJWoaC9eHaUs0NeXHYrn7mj09AWHmlpWaZoPEk0XVTMFzY++xRCzC+pNaFoqoVFzteVC0TiXK9AJ+9n4fNH4kBq1V2jizZKS8syVbrtvHdzE4OeEN2yqJUQpmazWnhkRwuXRv1SbDED1jWUcWhNLeF4kj3y+Vfw7t/YwNGeKdpqSmYGuRtFkpYV2NxSIVU3hcgT8xVbFEunlGK/1FErGtWlDtMs4ihJyyLEE0m+c2KQEW+YBzc3sr6x3OiQhBAZ8LNzI5wZ9LJ7VTWH1tTlbLs/fmeYiyM+9nfVsrdTynuIm10Z8/Ojd4apLXXwkV1tOGwykuM6eScWYSIQpW8ySDSe5FT/tNHhCCEyIJnUnOybJpbQvJ3D81prODPozfl2Rf54Z9BLJJZk0BNmRFZXv4kkLYtQW+qgvcaNw2Zha6v0hwtRCCwWxfa2Smzpn7miFGxqrsj5dkX+2NJSgcNmoaXKRWOFy+hwTEW6hxbBZrXwmEyRFKLg3L+pkfs35b6v/qGtTTy0tSnn2xX5oau+jN++d63RYZiStLTM4e1+Dz87NzIzzUsIUTz6JoO8eGaEgQIstnh+2MeLZ0aYChRfzRqxfOeGvfz07Igpah3lPGlRSj2llHoj/e/JOe5/WSn18/TP+3Id36g3zE/PjnKyb5pfXBjL9eaFEAZ77u1BTg1M8/2Tg0aHklH+SJwfnh7i1MA0LxRxIUmxNN5wjB+dHubt/mleNEHhTSO6h36itX5aKWUH3gC+Msdj7tdaG9LM4bRbsVsVsYSmLIfLJAshzKHcaSMSi1LmKqzz32ZROG1WwrEE5fLZJhbJYbXMHDdm+E7MeQRa62vpX+Ppf7MlgReVUsPAb2mtJ3MVG0BliZ0n9nUwFYzRJWs6CFF0HtvTTv9UkLZqt9GhZJTLbuXJfR2M+cN01spnm1gcsx03Ro5p+U3gu3Pc/pjW+h7ge8Afz/XEdBfTEaXUkbGxlXXheMMxhqZv7ruuLXOytqFMih4KUYRKHFbWNZZT4ni3vkoskaR/KjhTuiPTEklN/1SQcCy7xRwr3XbWNpRjs8pwxmITiMQZ8ITQWt/+wbOY6bjJWkuLUqoJeHbWzcNa648rpfYD7wc+PPt5N7SsfBv49FyvrbV+GngaoLu7e+l7IG06FOOf3ughGk9y57o6WeRJCDGn754YpG8ySGOFiyf3d2T89X/yzjDnhn1Ultj51KHOm6pTC7FS4ViCf3qjh2A0wa6OKu7Z0GB0SMuWtaRFaz0M3DP7dqVUK/AXwAe11rdcViilKrTWXuAO4HK24gPwhmIzV04T/kg2NyWEyGPj6c+HyUAErTVKZTapGE/P5vGGY8QSSawW46roisITjCYIpguGTviNnwG0EkaMqvm3QCPwrfSJ/z5gA7BHa/23wM+UUiEgzDwtLZnSVl3C/q4apgIxDuZwCW8hRH75lS1NnBqYZnNzecYTFkgVpDvSM0VXXSkuuyQsIrNqSh3cvb6eQU+IA3leM8qIgbifn+PmE+l/aK27cxWLUiqn9UaEEPkp28UWW6pK+GBVSdZeX4g9q6rZsyr/K3IbP38pR6LxJN9/exBvKMZDW5tpqpSlkYUQy3eiz8ORa5Nsaq7gjrUrv/iZDsZ47u1BrBbFB3a0mGJ6qSguI94wPzo9TJnTxiM7mnHazNfqZ/xQ4BzpmwrSMxFkKhjjZL/H6HCEEHnu8NUJfOE4h69Okkguez7AjDNDXsZ8EYanw1wY8WUgQiGW5u3+aSYDUXong/RNBo0OZ05Fk7Q0VbioKLFjtSjW1JcZHY4QIs+tbywHYE1DWUZm+6yuK8Vhs+CyW+moKaw1YkR+WFNfitWiKHfZaKo0Z3dl0bQ/ljptfOZQJwmtsZtgrrkQIr/ds6GBQ2vqcNgy83nSVOni83d3oZSSKc/CEF31ZXzhnjVYlTLtOmUF++19emCaN69MEEu8uxiUxaIkYRFCZMzshGUqEOXVS+P0Ty3ctD7iDfPqpXHGfDcvtWCzWiRhEYayWy0zCcv5YR+vX57I+qKHS1GQLS09EwFeOJMqCBZP6owMkhNCiNv5wekhRr0RjvdO8fn3rJn3IunbxwcIRROcG/bxuTtX5zhKIW5vxBvmB6eGAAhG49y/qdHgiFIKstnhxqWGbXLVIoTIEbsl9dljs1qwLLCey/XPJYdVPp+EOVktiuuHsBmW77+uIFtaWqtK+OjuNvyROBubyo0ORwhRJB7Z0cylUT9t1e4Fu3ke39NOz2Qgq2u/CLESdWVOHtvThicYY1NzhdHhzCiIpCUYjRNLaCpL7DO3ddTK6HshRG65HTa2t1XddJvWmslAlIoS+0x3UaXbznZ31a0vsEzhWIJQNEF1qSNjrylEW7WbthysR5dIajzBKFVux23HdOV90hJPav7Xq9eIJZI8sr2ZtQ3SsiKEMI+Xzo9ysm+aunInT+7LfLHFUDTBl9/swReOS+FXkZeeOznI1fEAq2rdPLq7bcHHmqejapniiSTReBKtYdATNjocIYS4ycBUCIBxX4RIPPOzMKZDMXzh+E3bEiKfDHhSx+2g5/bHb963tDjtVjY1lxOOJdnZUWV0OEIIcZO71tVz+Ookq+tLcTsy/5HbWOFk96pqRr1hDq3J72J4ojjdu6GB0wPTbGm9/dgZpfXKl582Ul1dne7s7DQ6DJF27do1ZH+Yi+wTc5H9YS6yP8zn6NGjWms9Z09Q3re0tK9axW/+1deIxJN8aGcLbdUyANdI3d3d/PmXf8CJXg+bmit4aGuT0SEVve7ubo4cOWJ0GCIt0/sjGk/y9aN9jPuiPLi5kc0t5pnpkQ/k/DDeT8+O8Hb/NFtbK3lwcyNKqWPzPTbvx7RE40l84TjReJLLYwGjwxHA2SEvAOeGveR7S54QZjcRiDDqjZDUmvMjXqPDEWLJrn9nXP+5kLxPWpw2K82VLqrddjabaC55MdvbWUOp08rezhrUAgtsCSFWrqHcRVd9KeUuGzvbczA/VYgMu/6d0d15++M377uHLAo+noVphGL59nbWyLRLIXLEalF8aGer0WEIsWz7u2rZ37W4QeR5n7TMRWvNa5cnCERS6xZkY8S+WJq+ySBv90+zvrGMdY2ylo4ROr/4vNEhFLVrf/rwzO+Xx/ycG/KxrbVSFsIUpvXmlQmmglEOra2jwmW//RNyoCC/zS+PBTh8dRJIVWG9Z0ODwRGJn5wZwRuKcXnMT1d9mVSyFUXth6eGiCU0/VNBPv+eNUaHI8Qt+qeCvHZ5Iv2XMs2kirwf0zKXyhL7zJditVuWtTaDmtJUll5ZYkfyFVHsqtKfS7LsvjCrcqcdhy2VIlS7zdHKAgXa0lJf7uSTB1YRjidoriwxOhwBPLK9hSFPmIYKpwzOFUXvsT1tjHojNFW6jA5FiDlVuu18Yv8qfJGYqZYSKcikBea+gpnwRwhEEtKHbAC71XLL+x5LJOmdDNJU4aLUWbCHohC3cNmtc34ODXpCWJSSZEYYpnciiNtppa7MSaXbTqWJWlmggJOW2cb9Eb7yZi+JpJaiYibxg1NDXBkLUO6y8Zk7Vss4F1HULoz4eP7tIZSCD+9spbOu1OiQRJE52jPJLy6MY7UontjXQX250+iQblGQY1rmEojESSRTC515QzGDoxHw7n4IRBLEk0mDoxHCWNfPB62ZKYAoRC55Q6njLpHU+CPmPAaLpqVlVW0pd66rwxuKcVCKipnCe7c0caLPw5r6Upw2q9HhCGGoHe1VBKIJrErJUvzCEAe6atFoypx2Ok06jKJokhZAuoRMprHCxa9sMcc0OiGMZrdaeM/6eqPDEEWsxGHlvo2NRoexoKJKWmaLxpN85/gAU8EoD21tYlWt9CEb7e1+D7+8NM7q2lIe2tokM41EUTjR5+HVS+OsqS/lV7bIcS8yK5HUfP/tQQY9Ye7b2MCGpvxd4NOQMS1Kqf1KqdeUUr9USv3VrPv+nVLqpFLqZaXU72UzjqHpEAOeEMFogtMDUmjMDE72eYjEkpwb9hGIJowOR4icONnnIRpPcnbIRygmx73IrMlAlCtjAcKxBCf7PUaHsyJGDcTtAe7TWt8JNCilts26//e11vdorf8ym0E0VrhoqHDisFnY1Jy/mWch2dJaiUUp1jSUUeqQcS6iOGxtrcCiFGsbyiixy3EvMqum1EF7jRurRbElz8dLGdI9pLUevuHPGDD70uI/K6WmgD/QWp/IVhwuu5Vf278KrbU0x5rE7o5qdrVXyf4QRWXPqhp2d1TLcS+ywmpRPLanrSC+6wyd8qyU2g7Ua63P3HDzX2ut9wBfAP7bPM97Sil1RCl1ZGxsLBNx3PT36YFpXj4/SsCkU74K3ez94QlGeencKBdHfAZFJET25eLL5OKIj5fOjeIJRrO+LWE+yz3GLqSPm+mg8cuFGJa0KKVqgL8BPnfj7VrryfTPi/M9V2v9tNa6W2vdXV+f2dH2o74wL5wZ4Xivh19cWHlCJFbuhTMjnOjz8INTw6ZdO0AIs/NH4jx/aogTfR5eODNidDgiT/jCMX6QPm5ePGv8cWPUQFwb8E+kun+GZ91Xkf5ZhwHdV06bFVt6ZVa3LC1vCteX+Lfb1My+EUIsjc2iZgrgSdkMsVh2q+WG48b48VZGHbmPA3uBP0s3V/0R8KTW+neAP1dKbSWVUH0x14FVltj5+L4OPMEoa+rLcr15MYcHNzeypr6MxgonLhmkKMSyuOxWntzXwYg3Qle9LO8gFsdlt/LE3g5GfRHWmOC4MWog7jPAM7Nufj193+dzH9HN6sudt9RcCEbjeENxKWRmALvVcsu6Alprhr1hqkoclMgsI5FnIvEEE/4ojRWunNbcqnI7qHLfWkxWmM+IN0yp00aZCVrFqksdcxYhNoLx70YeCEUT/OPrPQSjCfatruGOtXVGh1T0fnFxnGM9U5Q5bXzy4CppgRF55atv9THhj7KusYxHtrcYHY4wmbeuTfLLi+M47RY+eWAV5S5zVVo2UtEUTFwJfyROML3Q2ZgvYnA0At7dD/5InJAsQifyiNapxb5APk/E3Ea9qeMiEkvileKZN5GWlkWoL3dyaE0tw96wtLKYxN3r63j98gStVSWmabYUYjGUggc2NXJp1M/ujmqjwxEmdGhNLQmtqSt10FpVYnQ4piJJyyLt75LK0GbSUO7iQztbjQ5DiGXZ2lrJ1tZKo8MQJlVd6uCDO6TbcC6StCyT1poXzoww4AnxnvX1dMlMI8P1TgT52bmRmerRFpkeLURWhGMJvv/2EOFYgvdva6ZGWjsL2uUxP7+4MEZbtZsHNjUYuqqujGlZpslAlHcGvXiCMY5cmzI6HAEc6ZlkKhjj3LCPMb+MFRAiWy6P+embDDLmi3BqYNrocESWvXV1Ek8wxumBaTwGr4orScsyVZTYaahITYte0yCtLGawtqEMpaCuzEG1TOsUImtaq0pwO1ILcXbVGb92h8iudY2p77jGChflLmM7aKR7aJnsVgtP7O0gmkjKdFuT2N5WxfrGchxWi3QNCZFFVW4Hn7tzNUnNzGqponDtWVXDlpZKnDaL4QUX5WhbAYtF3ZKw9E0Gef3yhNTIMYjLbr0pYQnHErx5ZYJLo34DoxLCGP1Tqc8jbzjzTfq2G5Z3F7kTjMZ5/fIEPROBnG7XZbcanrCAtLRkVDAa59vHB0gkNUPTIR7d3WZ0SEXv5xfGODPoRSn4xIFV1JU5b/8kIQpAOJbg28cGiCc1fZNBPra33eiQRAa8cGaEK2MBLErx2Ts7i27hOUmTM8ii1MyS3LlcmlvM73qBRYXCaoKrBCFyxaLUTKujfB4VDpsl9bVtUal9XGykpSWDXHYrj+9pY3A6zMZZtXKEMe5eX099uZMaE9XOECIXHDYLH+tuZ8ATYkOjfB4Vigc2N9BWXUJjhasoq3UX3/84yxoqXDRU3FxUMZ5I4o/EpVCZAexWC9vbqm65fToUw2W34LTJIGpRuOYq/rpU08EYJQ6rjF8xCafNyo72qjnvi8QThGNJKksKt8tIkpYsSyQ1z77Vx5gvws6OKu7d0GB0SEXvVP80L54dodRp5cn9q0xRRVUIMzp8dZJXL41TWWLnyf0dMlPSxELRBF9+swdfOM49G+rZVaAlIiR1zrJwLDFTFK1/MmhwNAJSMyoAApEEk/6owdEIYV7Xz5XpUCwrM5BE5kwGo/jSxRX7pkIGR5M9comZZaVOG4fW1HJ1PCD1i0yiu7MGXyROtdtBW7UUIxNiPge6aoklkjRVllAvM+9MraXSxY72SsZ9UQ6srjE6nKyRpCUH9nfVSsJiIvXlTj7WLdM/hbidlqoSfnVvh9FhiEVQSnHfxkajw8g6SVoMcuTaJBdH/eztrGGtlAEw3GQgyk/PjlBRYueBTY0yRVQUtXgiyQtnRghEEzywqUEmERjsWO8U54d97FlVzfoinwm24JgWpVSFUmrNHLdvX+mGlVJ/pZR6RSn1X2fdvlUp9Uul1KuZ2I4ZhWMJXrk4zvB0mFcujhkdjiCVRPZPhTgz6OVajleaFMJsrowHODfso28yyLFeKQhrpHgiyS8ujDE8HeYXF+T7Yt6kRSn1MeAc8E2l1DtKqb033P33K9moUmo3UKa1vgtwzHrt/wA8AXws/XvBcdosNFWmpkW3VbsNjkZAaj8oBSUOq6yaK4peQ7kTp92CUvIZZTSb1UKzfF/MWKh76P8E9mith5RS+4B/VEr9kdb628BK284PAC+kf38ROAi8lf67WmvdB6CUqlrhdkxJKcXje9rwheNUuQt3Pn0+2dxSQVtNCQ6rRaZ1iqJX5Xbw2TtWE0ski26ZeDN6bE873lBMvi9YuHvIqrUeAtBaHwbuBf5YKfUvAL3C7VYB3vTv0+m/54ppzuRIKfWUUuqIUurI2Fh+NpfZrBaqSx03FaCaDEQ53jslxRYNUuGy35SwJJOa0wPTXB2X7iJRfFx2a9YSllFfmBN9HkLRRFZev9BYLeqW74vb6ZsM8na/h3gimcXIcm+hlhafUmqN1voyQLrF5V7g28CWFW53GqhI/14BeG6478aEaM53W2v9NPA0QHd390oTKFNIJjVfP9JHMJrg7JCPJ/fLiH2jvXVtktcuTwDweHebNM0KkQGReIKvH+knGk9yddzPR3ZJYdlMm/BH+OaxfrSGiUC0oBY1Xail5QuARSn1O0qpagCttRd4CPjsCrf7OnB/+vcHgDduuG9SKdWmlGrh3daYopDQqfwrkSyszDhfJZJ6zt+FEMundeoiDSCekPMqGxJak/46IVFg7/G8LS1a65MASqlPAW8ppY4Bfwf8WGv95ZVsVGt9TCkVVkq9ApwAepVS/1pr/SXgT4Cvph/62yvZTj6xWBSP7mrjyrifTU0Vt3+CyLq9q2tw2Cy4HTZW1ZYaHY4QBcFlt/LhXa30T4XY2iqfddnQUO7iAzuamfBH2dlRZXQ4GXXbdVq01n+slPo3wHuBzwB/o5T6GvC317uOlkNr/buzbvpS+va3gTuW+7r5rKnSNTOr6EbhWEIGhxrAbrXQ3XnrypLReBKLSo1LEiLfhGMJnDbLksZHZFp7jZv2mvzvbk0kNYmkNmUxybUN5awtnF6hGYtaXE5rrZVSw8AwEAeqgW8opV7QWv9hNgMsdt9/e5CLI362tlby4ObCX+3Q7Pomg3zn+AAOm4Vf3dsui26JvPLS+VFO9Hroqi/lQztbjQ4nrwUicZ453EsgkuDh7U2sbSjuRd9y5bbpoVLqd5VSR4E/A14FtmmtvwDsAT6a5fiKmtaay6OpmSsXR30GRyMgtehWPKkJRhMMeAq3KJkoTJdG/ABcGQsU3KySXBv2hvGF4yS15vKYzDDMlcW0tNQAj2qte268UWudVEo9kp2wBKTWcznQVcM7g152FVi/ZL7a1lpJ32QQl93KmnopvyDyy4GuWt66NsnGpnLp3lyhjho3XfWleEMxdrZXGR1O0VjMmJY/WeC+s5kNR8wmxRbNpabUwScOrDI6DCGWZVtbJdvaKo0OoyDYrRbpYjOAFEzMQ5dG/ZwemGZra4X0o5pAOJbg5fOj2CwW3rOhHrtcwYoilEhqfnFhjFAswT0b6nE75OtlNq01r12eYDIQ5a51dTImbhnkqMpDPzkzTCSWZMATkqTFBI73ejg7lBpz1FTpYmurXMmK4nNhxMeJPg8A5S4bd62rNzYgE+qfCnH46iQANovifduaDY4o/8glYR6qTxf0q5fCfqZQX+5AqdRS2zWlcuUkilNtqQOrJTWNur5cPpvmUum2z0yPbqiQ92g5pKUlD314Vytjvoh8MJjE2oZyPnnAgc1ioVIKmoki1VDh4lOHOoklklIpfR4VLjufOtRJMBKnoeLWNbnE7UnSkofsVgstVSU33RaOJbg2EaCt2k2ZU3ZrrtXO8SHdOxFEKQpiES0hFqOy5OakvW8yiNbQUVuc54AvHGPAE6KztnRmgdAyp00+o1dA3rkC8e3jAwxPh6kssfPZO1cbHU7ROz/s4wenhgD4wI4W1jbI9GhRXC6N+njuZOoceHh7M+sbi2v8XTKp+epbffjCcdqqS3i8u93okAqCjGkpEMF0ifdQLIHWhVUgKx8FovGZ30PpfSNEMQlE3j3ug0V4DiS1njn3Q7Hi+/9ni7S0FIiHtzVzZmiadQ3lhtYUESnbWysJxxJYlGJzixSFE8Vna2tl+iIqtShjsbFZLXxwZwuXx/wyozCDJGkpEPMVWxTGsFktHFpTZ3QYQhjGalEcKPKFMVfVlkqF+AyTpKWAvX55gsNXJ9nQVMZDW2U9AKNNBqJ861g/Sa15dHebzLAQYomC0ThfP9KPPxLngztaCmaQezyRnBmXeP+mRmmdXYCMaSlg7wxOk9Sas0M+YlIczXBXx/34wnECkQSXR/1GhyNE3umfCjEZiBKNJzk/XDhFZCeDUfqnQsSTmjNDXqPDMTVJWgrYzvYqHDYL21orZWl5E1hbX061205liZ11RTaTQohMaK9201jhotRpLajWiNpSJ6vrUtOit0ttqAVJ91AB6+6sobuzxugwRFql286n75Dp6EIsV4nDypP7O4wOI+OsFsWHd0nxxcWQy+8iM+oN8+N3hrk4UjhNq/kskdS8emmcl8+PEo1LF54oPBdGfPz4nWFGvWGjQ1myWCLJLy6M8crFMeLSxW4KOW9pUUo9BXw2/edfa62/Muv+lwEFaODfa61/ltsIC9uPz4ww7otwbsjHb9a6cdqsRodU1M4OeWcKqJXYrewv8tkWorCEYwl+eGqYpNaM+SJ84sAqo0NaklMD0xztmQKg3GVnZ3uVsQEJQ7qHfqK1flopZQfeAL4yx2Pu11rH57hdrFCFy8a4L0Kp04rNIg1tRqsssaMUaI3ULRIFx2ZRlDqt+MJxKkry7/iucNlv+F1GU5hBzveC1vpa+td4+t9sSeBFpdQw8Fta68lcxVYM3r+tmd7JIE0VrpmKrMI47TVuntjXQTypaZ1VT0qIfGezWvj4vg5GvGE68nB68tqGMn51bztKQXOlnJ9mYGTq+JvAd+e4/TGt9aRS6kngj4Hfm/2AdBfTUwAdHYU3KCub7FYLa+pvroOTSGoGpkLUlTtwO+RqItca56j2OuGPEEtoWTBQ5L0yp42y+uXV3jLDeTC7OC3AqC81PqehXM7PXMvaN5RSqgl4dtbNw1rrjyul9gPvBz48+3k3tKx8G/j0XK+ttX4aeBqgu7tbCu2s0Atnhjk75KPcZeNThzplerTBhqZDfO2t1CJ0v7KlqaCmdgqxWIOeEF8/kjoPHtraxKZmc5wH18YDfOfEAAAf3NFC1zITMrE8WUtatNbDwD2zb1dKtQJ/AXxQa31LFSmlVIXW2gvcAVzOVnziXZOBGAD+SJxoPClJi8GmQzGS6aKXU8GowdEIYQxP8IbzIGCe82AqGOV6TdqpYMzYYIqQEX0B/xZoBL6VLuz3PmADsEdr/bfAz5RSISDMPC0tIrMe2NTAW9emWFXrptQp3UNGW9dQzlhnhHAsyZ5V1UaHI4QhNjSVM+6PEI0n2W2i82BrayWeUAyKtBCk0YwYiPv5OW4+kf6H1ro7l/EIaKhw8fB2qU1kFlaL4q519UaHIYShrBbF3evNdx7YrRbu3dBgdBhFSy6rxZzOD/v4xYUx2qpLeGhrE+lWMWGQcCzBd08MEIwmeHhbMw1zDN4VIt95glGeOzmIUooP7my5acpxNg1Nh/jhqWHKXDY+tLNF1q8yMRm8IOZ0rHcKfyTOuWEfHum3NVzPRJBBTxhPMMbpwWmjwxEiK84N+xj3RxnzRbg4kruioqf6p5kOxRiYCtE3GczZdsXSSdIi5rSxqRyloLW6JC8XhSo01/eD3apY1yDFFkVh6koXDXQ7rHTW5m5dl3WN5dgsisoS+5xTnIV5SPeQmNOujmq2t1XJAnQmUea08dk7OtEaLLJPRIFqqHDx+bu7UIqcdkmvrivlt+5diyXH2xVLJy0tYl6zExZ/JM4rF8e4NCrFFo2glLolYTk9MM1rl8aJxG9ZPUCIvGSxqGUlDhP+CL+4MLbs7h3rrO2GoglevTTOmUHvsl5PZIe0tIhF++nZEa6MBVAKPnPIJbVyDNY3GeSFMyMARBNJ7pEZDaKI/eD0MOO+CG/3e/j8e9aseL2pVy6O8U46Yaktc8y5crXIPWlpEYt2fUS9zaKwWqUJ1WgOm4XrF4Yy20EUO6ct9XVmt1qwZKCLx2lPnVMWpWTBTRORlhaxaPdvaqC9poSGchdlsgid4RorXDze3Y4/HGddgywlLorbB7a3cHnMT1t1SUbG4t25to6GcifVbgc1pY4MRCgyQb55xKLZrRa2tNy6AuS4P0KZ04bLLlf7uTZXZehgNE4omqC2zGlAREIYo8RhZesiV6j1hWMkkyzYxW21KDY1VzAdiuENx3K2ZoxYmCQtYkXevDLBa5cnKHVa+eSBTqPDKXrecIwvv9FLOJbg3o0N7GyvMjokIUxl1Bvmq2/1kdCaR7a3LPjYnokA3zk+iFLw0T1tc14kiNySjjqxIsPeVIn2QCSBNyyL0BnNE4gRjqVmEg1PhwyORgjzGfVFiCc1WsNI+vNrPiPeCEmtSSQ1o7d5rMgNaWkRK3JwTS2JpKaxwiWj602gvaaEXR1VTAWj7F9da3Q4QpjOhqZyBjwhovHkbVsit7VWMuaLYFGwuaUiNwGKBSl9vcZ2nqqrq9OdnZ1Gh1HUNHB92Nu1a9eQ/WG82ftkVWcnMt/LHOQcMRfZH+Zz9OhRrbWesyco71taOjs7OXLkiNFhFK3TA9O8eHaE2jInv9rdzqED+2R/GCgUTfDsW714Q3Hev62JdY3l7Ni1h8/++bNYlOJj3W0yQNdg3d3dco6YyEL7Y8Qb5htH+7EoxePdbdTJuZMTSqlj890nY1rEilwc9aE1jPsiTAaiRodT9Ea8qaKKSa25OJoqOBeNJ4jGk4RjCXqkGJwQi3Z1PPDuuTMh544Z5H1LizDW7o5qpgIxGiqc1JfLVYjRWqtLWF1XylQwyo50f73LbqW+3InVoljfKMUWhVisTU0VXB7zY1GK9Y2yFpIZSNIiVmRVbSmfvXO10WGINLvVwod3td50m9Wi+MSBVQZFJObT+cXnF/W4a3/6cJYjEfOpdNv5tf1y7piJdA8JIYQQIi9I0iKEEEKIvCBJixBCCCHygiQtQgghhMgLkrQIIYQQIi/I7CGxaGcGvfzs3AjNlSV8eFdrRsq/i+ULxxJ8/Wg/vnCMD2xvob3GvejnTgWifPNYPwCP7m6jptSRrTCFyEuXx/z86PQwNaUOHt3ditMmVezNQFpaxKK9MzhNLKHpnQzKQnIm0D8VYtwXIRJLcnbIu6TnXh7z4wvH8YXjXB7zZylCIfLX2SEv0XiS4ekwo96I0eGINElaxKJta6vEYbPQWeeWK3MTaKsuoaHCSYnDyqbmpRVzW1NfRmWJnYoSO2vqZdEsIWbb0lKJ026hpUqKwZqJdA+JRdvYVMHGJql0ahYuu3XZC19VlzpkUUAhFrC6rpTfumet0WGIWaSlRQghhBB5QZKWAhaKJjg9MM10MGZ0KCLt6nigYMeQ9E8FOT/sQ2ttdChC5J1RX5gzg15iiaTRoZiadA8VsOdODjLgCVHqtPIbd3Zhkdk+hro44uP7bw8B8NDWpiWPQzGz4ekw3zjaj9YwFazlQFet0SEJkTf8kThfe6uPWELTN1XBr2xpMjok05KWlgIWjicAiMaTyLWv8cKxd6+gIvHCupqKxpNcb2AptP+bENkWTySJJ1MnkJw/C5OWlgL28LZm3hn00lVfKmuqmMCWlgoi8QQa2NZaaXQ4GdVR6+aBTY34IjH2rKo2Ohwh8kqV28Ej25sZmg6zq0POn4UYkrQopfYDfwUkgbe01v/HDff9O+AjwBTwPa31XxoRYyGoLXNy9/r6RT1Wa41Skthkk8Wi6O6sWfTjzbRPFhPLtrbCSsSEmM/1cVuZPD/XNpSztqE8Y69XqIxqaekB7tNah5VSX1ZKbdNan7rh/t/XWr9oUGxF52jPFK9cHKOztpQP7miRsS8G84ZjfP1IP5F4gg/vbKWlqsSwWKZDMb5+pI9oIslHdrXSXGlcLEKYwYQ/wjePpcZvfXRPG3VlTqNDKiqGjGnRWg9rrcPpP2NAYtZD/rNS6kWl1M7cRlaczgx50To1syUQjRsdTtHrnQjiDcWIxJJcGjV2plHfZBBfOG6KWIQwg6vjAQKRBMFogqvjAaPDKTqGDsRVSm0H6rXWZ264+a+11nuALwD/bZ7nPaWUOqKUOjI2NpaLUAvarvYqXHYrm5rLKXOmGt/iidSX1HRIpkvn2uq6UhoqnFSU2NnY/G5z8ag3TM9Edj8kh6ZD9E0Gb4qlvtxJZYldFhYUBUtrzbXxAGO+2y/Xv66hnNoyBzWlDtY1yGrSuWbYQFylVA3wN8DHbrxdaz2Z/nlxvv5CrfXTwNMA3d3dMjFmhba2VrJ11sDQF8+OcnbIi8tu5TN3dOKyS7GwXCl12m5Z6XbEG+bZw30kteaeDfVZGazXMxHgW8cGAHjftiY2NlVQ6rTxiQPLW3VXiHyR6iIfx6IUT+7voL58/i6fSredXz/YmbvgxE0MaWlRStmAfwL+QGs9POu+ivTPOmR2k2F84VQLSySeICqLHRnOH4mTTA/+84Wz04V34+tmaxtCmNH14z2pNYGIHPtmZlRS8DiwF/izdGvKHwFPaq1/B/hzpdRWUgnVFw2Kr+g9sKmRIz1TtNeUUOGyGx1O0euqK+WOtXUEo3H2rV78DKSl2NRcgTccI5HU7Gyvyso2RPHp/OLzt33MtT99OAeRzO/6YojlLhuddaWGxiIWZkjSorV+Bnhm1s2vp+/7fO4jErNVlzp4cHOj0WGINKVU1pKV66wWxaE1dVndhhBmVOKwcu/GBqPDEIsgK+IKIYQQIi9I0mIivRNBfnZuhFFv+PYPFlkXjiV45eIYR3umjA6lKJ0d8vLSuVG8YZnBJsRSneqf5uXzowU3RkcGuppEIqn53skBYglNz0SQz9yx2uiQit5b1yY5ci2VsFS77XTVy/TGXPEEo/z4nWG0Ti2296GdrUaHJETeGJ4O8+LZESBVy6iQCjBKS4tJWBQz04pLHZJLmoE7vR+Uevd3kRt2qwW7NfXxJO+9EEvjsltm6s0V2vdJYf1v8phSio/v62BgKsSqWrfR4Qhgz6pqqt123A4bTZUuo8MpKqVOG0/s62DCH5EWLiGWqMrt4Il9HUyHonTVFdb5I0mLiZQ5bWxoym7BrHgiSf9UiIYKp1zBLkIuvjDHfBESSZ1XidGEP0I4nqQ1i3WRakpTq44KYaQRbxgFNFTkz/kJUF/uXHCRvHwl31oFJBiNU2K3Llh59Ienh7k06qeixM6nD3XONCGKzIvEEygUDtv8vbB9k8GZ4mvv39ac9aQ1E25cnfeBTY0LVneOJ5LEk1pWVBZ56fKYn++dGEQp+NDOVlYvcQ0XrTWhWEIuEDNI3skC8ZN3hnln0EtXfemCgxY9wSgA/nCcWCKJ1SJfJtkwNB3im0f7UUrx+J62ea/SpkMx0gvdzuwbs/OGYjOr804tEHMgEueZw70EIgl+ZWuj1C4SeccTTM1c0/r6+bn4pEVrzbeODdA7GWRne5WsA5MhkrQUiCvpaqNXxwMkkxrLPC0oD25u4njvFKvrS+XqN4t6J4LEEhrQ9E2F5k1aNjVXMBmIEk8m2dlRldMYl2tNfRn7V9cQjCbY2zn/gnejvsjM8ujXxgOStIi8s72tEm84hoJb6rPdTiyh6U0XH7085pekJUMkaSkQh9bUcrzXw+aWinkTFoCmShfv29acw8iK0+aWCnomgigFGxfo8rFaFHevr89hZCtnsSgOrb39yrnt1SWsayzDE4yxOwsFHoXINrvVwr0blpdsOGwWDnTVcmHER3enHP+ZIklLHro06uedwWm2tFSyNl0afXtbFdvbqowNrEiFYwlePj+Gw6a4e109NquFcpedj+1tNzo0Q9msFh7Z3nLL7a9dHmcyEOXOtXVUuWWgrShcB9fUcnBN7czfWmtevzzBZDDKXWvrqXRLXbelkqQlD/3kzDCRWGoW0NqGtUaHU/SO93o4O+QFoKHcteRm5GIy4Anx5pVJACxK8X5p9RNFpH8qxJtXU8e/VSlp9V4GWVwuD9WVOdM/5SrVDOrLHSiV+hKWKboLq3DZcNpTHzvXj2MhikVFiX1mNmEhTkfOBWlpyUMf2dXKqC9Cgxz0prC2oZxPHHBgVYpqSVoWVO6y8+sHO/GH43m1Lo0QmVBZYufXD64iEEnI8b9MC7a0KKWalFJN6d/rlVKPKqW25CY085vwRzg75CWWSOZ0u3arhdaqkpllzkVKPJHk3LCXMV8k59uuK3NKwrJIZU5jVhieDsU4M+glHEvkfNtCXFfusuddwhJLJDk75GUyYPyyDPN+6ymlPg+8DryhlPoC8H3gYeBbSqnP5Sg+0wpG4zz7Vh8/Oj3MT8+OGh2OAF4+P8YPTw3z7OFefFIZWNwgmdR87a0+fvzOMN87OWh0OELklRfPjPCj08M8c7jX8KR/oe6hfw5sAUqAHmCt1npYKVUNvAT87Uo2rJT6K6AbOKa1/t0bbt8K/A9AAV/QWr+9ku1kSyyhZ1pYjN6JIiWU3g8JrdNrpAiRktSaSDx1fMj5KsTSXP9sjSWSxBJJQ9f4WihpiWmtg0BQKXVZaz0MoLWeUkqt6BtBKbUbKNNa36WU+u9Kqb1a67fSd/8H4AkgCfy/wIdWsq1sqSyx8/C2Zganw+zKk0XBCt29GxuoKLHTWOGUAbHiJjarhQ/tbOXSmJ9tMrtLiCW5f1Mjx3unaKsuodxl7DTthZIWrZSya61jpLqFAFBKuVj5rKMDwAvp318EDgLXk5ZqrXVfeltVK9xOVq1rLGddo/lrxRSLMqeN9+TZQm0id9pr3LTXSAV1IZaqssTOPctcZC/TFkpaPgKglPod4J9uuL0W+P0VbrcKuJL+fZpUN9R1NyZEcy7tqpR6CngKoKOjY4WhCCFE/uv84vNGh7BsmY792p8+fPsHibw0b4uJ1ro33crSCLyllPqaUuohYFBr/eIKtzsNXC9EUgF4btz0Db/POS1Ha/201rpba91dX2/+K+uXz4/y969e5fywz+hQBKlihv/4+jWeOzlIPMczv0TmvZQ+vy6OyPklilciqfnBqSH+4fVr9E8FjQ4na27bzaO1/mNgHamBt58GLiql/qNSas0Ktvs6cH/69weAN264b1Ip1aaUagG8K9iGKfgjcY73epgKxjh8dWLm9lFvmLeuTcosFwMc7/Uw7o9yadTPgCcEpKZLH++d4tKo3+DoxEIGPCGOXJskFE0NDPSGY5xIn1/XVxoVotCM+lLfF9Oh+b8vhr1hzg/7mPBHOdbryV1wObaosSlaaw0Mp//FgWrgG0qpP1vORrXWx4CwUuoVIAH0KqX+dfruPwG+Cnwd+LfLeX0zcduttFaVAKnquJD6gvz60X5+eXGc598eMjK8otRVX4pFKSpK7DSUp9ZLeP3KBC+fH+O5k4MMphMZYS6BSJxvHe3nlYvj/OTMMAClDhstVal9eP38EqKQJJOabx4d4JcXx3lugen6taUOqt12lIKuutIcRphbt10RVyn1u8CvA+PA/wT+ldY6ppSyABeBP1zOhm+c5pz2pfTtbwN3LOc1zchiUTze3UYkbuw0MfGujU0VdNaWYrdasC5QEVuYn9WieHxPO1GDp2EKYTSX3conD3YaPiU52xazjH8N8KjWuufGG7XWSaXUI9kJq7AopW46iGxWC4/vaaN3MsiGJpl9ZITZJ/XBrlrKnDYqSuy0pFvGhLmUOm08uqeNIU+ILS3vTlu2WBQuS+F+SIviZrEoHtvTRs9E4LazVa0WhbXAz4XbJi1a6z9Z4L6zmQ2neDRUuGioyK+lnAuZzWphV0e10WGI22itKpnpbhWiWNSXO6XAYpoUrxFCCCFEXpCkRQghhBB5QZIWIYQQQuQFSVqEEEIIkRckaZlDLJE0zaJvwWiciyO+oq9M6w3HTLN6bc9EgOHpsNFhFJTJQGqxv2TS+OrcyaRmOhQjtTyVEPMb9YW5MuYv+GMlnkjiNcl34mKmPBeVUDTBl9/swReOc9/GBna0Vxkaz9eP9DMZiNJU6eKJfcVZZ+m1S+O8eXWSunInT+xtx2Y1Ltd+u9/DT8+OohQ83t0uM1kywBeO8ZU3e4glNLs6qgwvzPbc24NcGQuwrrGMR7a3GBqLMK8xX4Rn3uwjqTV3rK1j3+oao0PKilgiyTOHe5nwRznQVcvBNbWGxiMtLbNMBqP4wnEArk0EDI1Fa40/korFn46pGPVMpupojPsiBCLGtjhd3w9ap1ZoFSsXjiWJJVJXqn4TvKe9E6njrWeicOu3iJULRRMk9fXj1hytENngD8eZ8EeBVCuz0aSlZZbmChdbWysZ90fYv9rYjFIpxQd3tHB+2MfmlorbP6FAHeyq5dXL43TUuKl02w2NZU9nNfGkxmW3sq5Blo3PhPpyJw9ubmTUF2Zvp/FXq3evr+fUwDQ72qqMDkWYWEetm7vX1+MNxzhg8HdFNlWXOti9qpr+qSCH1tQZHY4kLbNZLIoHNzcu+/kn+jz0TgbZv7qGxgwsHtde46a9xr3i18lnnXWldC6zlsZUIMqrl8epK3NyoGvlHyxOm5W715u/sni+2dpaCVTe9nGLdXpgmstjfro7a5bchbejvcrwbmGRH/asyv2ClGO+CK9fmaC1qiRn23+PiT7zpHsog7zhGC+dG+XyqJ+Xzo0aHY4AXr08zsURP69fnmBoWgohFoNwLMGLZ0e4Mhbgp2dHjA5HiIz6+YUxLo/6+cWFMaYCUaPDyTlJWjLIZbNS7ko1XtWVyZLLZlBbmtoPTruFcpexXUsiN+xWC5UlqX0t56EoNLVlDgBKnVZKHIVdZ2gu0j2UQQ6bhV/bv4rJYJSWSqkrZAYH19TSWeem3GWnzCmHezGwWhRP7OtgIhClSep7iQJzz/p6NjSWU+W2F3Q15/nIp3iGlTistDpkGqyZNFfK/ig2LrtVpqOLgqSUKupK9AXRPdQ7EeTCiK/gF/jJF/5InNMD00yHCncaoBCLFY4lOD0wzWQRjj8QYjF84RinB6YXtahrzltalFJPAZ9N//nXWuuvzLr/ZUABGvj3WuufLfR60XiSbx7rB+A9G+rZ3ZH70dziZt8+PsC4L0JFiZ3P3bna6HCEMNQPTw9xbTyIy26V8yFHOr/4/G0fc+1PH85BJGIxvnVsgMlAlCq3nc/csfA5YkT30E+01k8rpezAG8BX5njM/VrrRa0ydWPbSiRmjmXei100npz5qbVGKWVwREIY5/rnUjyRnFmMTAjxrhu/M24n50mL1vpa+td4+t9sSeBFpdQw8Fta68nZD0i31jwF0NHRwX0bGwjHEuw2YM68uNUHdjRzftjH2oYySVhE0XtoaxOnBqZZVVNalAMnhbidD+1s4fyIj/WN5bd9rJEDcX8T+O4ctz+mtZ5USj0J/DHwe7MfoLV+GngaoLu7W8tCUObSUO6ioVxmbQgBUOV2cNc68yzOJYTZNFS4aFjkTL+sJS1KqSbg2Vk3D2utP66U2g+8H/jw7Ofd0LLybeDT2YpPCCGEEPkla0mL1noYuGf27UqpVuAvgA9qrW+pfqeUqtBae4E7gMvZik8IIYQQ+cWI7qF/CzQC30qPd3gfsAHYo7X+W+BnSqkQEEZaWoQQQgiRZsRA3M/PcfOJ9D+01t25jEcIIYQQ+aEgFpcTQgghROGTpEUIIYQQeUGSFiGEEELkhaJKWrTWJJKyIqWZxBOyirHIX3L8ikKTSGpT1/ErmirPwWicr77Vhz8c5+HtzXTVlxkdUtF77fI4b16ZpKu+lA/uaJHVc0Ve+cWFMY72TLG+sZyHtzcbHY4QK3Z1PMD3Tw5S6rTxq3vbKXWaL0UompaWoekwnmCMeFJzcdQPpK6Snjs5yD+8fo2h6ZDBERafc0M+AK6MBYika06cH/bxv169ysvnR40MTYibHO+d4n+9epW3rr1bVeTcsBeACyM+acEVpqa15ifvDPP3r17l2nhg3sddGvUTT2qmQzHTficWTdLSVl1Ce42byhI721orARjwhLg06mfCH+V4r8fYAItQd2c1pU4rO9urZmqyHL46gScY43ivB39kUTUzhci616+kjsvXLk3MNJ13d9ZQ6rSyZ1U1Vou0EgrzmghEeWfQy1QwdlPiPdu21koqS+wz35dmZL62nyxx2qw8tqftptsayl1UlNjxh+N01ZcaFFnx2t5Wxfa2qptuW9NQxrh/ktaqEtxSXE6YxNr6Mt4Z9LKmoXSmG3N3RzW7O6RIqzC/yhI79eVOxnwR1jTMPzSiqdLFZ+9cncPIlq5gk5apQJRwPEFzZcm8jylxWPn0oU7iySRO28JfkKFoAl84tuiiTuJmsUSSIU+YhgrngpVuD62pY3dHNU6bZcExLlprRn0RKkvsUjlXLFsskWTCH6W+3Llga8l7tzRx9/p6nLaFG6fjiSSDizjOhbidUV+YUoctI+NK7FYLT+7rIJpILum4nA7F8IVjtFWbp9WlIJOWMV+EZw73kkhq7t/UcMvV/I2sFoXVcvuE5R9ev0YwmmD/6hoOra3LcMSF7/tvD3JtPEhNqYNfP7hqwYRkMSfVKxfHOdozRZnTxicPrpIvCLEsXz/Sz4g3TFd9KR/a2brgYxdzjP3g9DCXR/1Uue186mAnFuk2Estw5Nokr1wcx2W38smDqyjLQOJisShct/muu9F0MMY/vdlDNJ7kjrV17Ftds+IYMqEgx7RMh2IzA+MmA9EVv54vEiMYTdV2HPVFVvx6xWgyEANS+yYTYxav7wd/JD6zb4RYimRSM+5PHUcj3nBGXnMq/XnjDcWJy+BcsUwj3tRxGY4lmA7FDInBF4kRTU+QyMT3aKYUZEtLV10p+1bX4I/EM5IdNpS7OLimlhFvmENrpJVlOd67uZG3+6dZ31iWkUGLd6+r47XLE7RUlVBT6shAhKLYWCyKBzc3cm7Yy44FWmOX4oHNjZzo9bCmoRTHbbqShJjPoTW1xJNJakudtFQaMyShrdrNoTW1TAWjHFpba0gMc1FmXkRmMerq6vSqzk7QIMt8GO/atWt0dnaiZX+YxvV9Ioxz4/mQrf0h59zyyPlhPkePHtVa6zmz/rxvaWnvWMVTf/FVovEkH9rZSketeQYMFaPu7m7+4//+Pu8MelnbUMYHdrQYHVLR6+7u5siRI0aHUbReuzTOm1cnaa0u4bHdbezbtzej+yMST/Ds4T6mglHu39jItrbKjL12MZDzw3yUUsfmuy/v2y+jiSTBaIJ4UnN1Yv5Fc0TuXBpLLd53ecxv6uWghciF6+fDwFSIUCzz468mA1EmA1G0Tp1zQhSyvE9aXDYrHTVuGiqcM4vGCWMd7Kqlym3n0Jo6WZpfFL19q2uoctvZvao6K8uiN5a72NhUTk2pgz2rZN0YUdjyvntIKfjorEXjhLF2dVSzSxbdEgKAjU0VbGyqyNrrWyyK922T2keZ0PnF52/7mGt/+nAOIhHzMV1Li1KqUyk1opR6WSn1E6PjEUIIIYQ5mLWl5QWt9SeMDkIIIYQQ5mG6lpa0e5VSryil/g+jAxFCCCGEOZgxaRkC1gP3Ag8opbbPfoBS6iml1BGl1JGxsbGcByiEEEKI3DNd0qK1jmitA1rrOPB9YOscj3laa92tte6ur6/PfZBCCCGEyDnTJS1KqfIb/rwDuGxULEIIIYQwD9MlLcBdSqmjSqnXgAGt9ZtGBySEEEII45lu9pDW+gfAD4yOQwghhBDmYsaWFiGEEEKIW0jSIoQQQoi8IEmLEEIIIfKCJC1CCCGEyAuStAghhBAiL0jSIoQQQoi8IEmLEEIIIfKCJC1CCCGEyAuStMwhlkjiC8du+7ih6RDHe6cIxxI5iKq4ecMx4onkgo+JxBMc752ifyqYo6hEPhn3RzjaM4U/El/wccmkZjoUQ2udo8hyJ55I4l3EZ9tiXBr1cXpgmmSy8N4nYV6mWxHXaKFogi+/2YMvHOe+jQ3saK+a83H+SJxvHOknntQMesI8vL05t4EWkdcujfPm1Unqyp08sbcdm3XuXPulc2OcHfJitSg+daiTyhJ7jiMVZpVIar5+pJ9wLMGFER9P7OuY97HPvT3IlbEA6xrLeGR7Sw6jzK54Iskzb/Ux7ouwf3UNh9bWLfu1ro0HeO7kEJC6WNizqiZTYQqxoIJtaZnwR5Z1xT0ZjOILp67Erk0E5n2c1prr1xeJArwiy7RoPMnV8QCh6NJbpXomU/tx3BchEJn/+devjLWmIK+Sxcok08dE4jYtA70TqeOtZ2Lpnx+xROo4D0YXbs0JxxJcHQ8QieeulTYQSTDuiwDvnlPLlbzh/LpNA6gQGVWQLS2jvjDPHu4jkdTcu7GBnfO0lsylucLF1tZKxv0R9q+unfdx5S47H97ZyrA3zLbWygxEXdieOzlI72SQaredTx3qRCm16Oce7Krl1cvjdNS4qXTP33py78YGasucNJQ7qXI7MhG2KBBWi+KxPW1cHQ+wqaliwcfevb6eUwPT7GirWvJ2fnBqiCtjASpK7HzmUCcWy9zH+deP9DHuj9JaVcLH9rYveTvLUem2091ZTe9kkINd83+2LUZXfRm/sqWJcDyxrPdJiOUqyKTFG4rPXE15gtElPddiUTy4uXFRj+2oddNR615yfMVoOpTqR/eG4yQ1WBefs9BZV0pnXeltH+eyW9m3WpqpxdwaK1w0Vrhu+7gd7VXzdgvfjieYOs794TjxpMYxT9Jy/XzwhJb2+bRSd62rz9hrbW5ZOPkTudf5xedv+5hrf/pwzl8rkwoyaVlTX8qBrloCkfhNrSX9U0F84TgbGsvnvQIS2fErW5t4u8/DusZyrOn3PhRNcHnMT2tVCdWl0jIi8kswGufKWID26ndbAN+7pZETvR7WNJThsM3f+/7+bc2cH/axdVYr7aVRHxal6Kovy2rsQuSrgkxalFIcXHNz8+eoN8w3jvajNUwFoxxas/xBaGLpWqtKaK0quem27789SP9UiBKHld+4c/W8A2yFMKPvnhhkeDpMmdPGb9y1GqUUzZUlNG8rue1zu+rLbklMTg9M88KZEQA+sKOZtQ3lWYlbiHxWNN8SkXiS62PHonEZOWYG0fQIvngiicyaFPkmkl7qIJp497NlJaI3jGiNyGeUEHMqyJaWubTXuHlwcyPecIw9q6qNDkcA79/azOnBaTprSxdsShfCjB7Z0cLZIS9r6ssy0t28o62KZFJjsSg2N8t4ESHmUjRJC3BL/7EwVnWpI6MDA4XIpboyZ0aPX6tF0d0pA8mFWIhc3gohhBAiL0jSIoQQQoi8IEmLEEIIIfJC0SctsUSSwG0KqInc8oVjt11qXYhc0lrjC2e3iKIc90LcXlENxJ0tEInzzOFe/JE4D25uZEuLDNQ12isXxzhybYrGChcf39suiwAKU/jh6WHOD/voqi/lQztbM/76P78wxrGeKZorXXysW457IeZT1C0tE/5UcUStl1ccTWTetfFUkcoRb5hgLHfF5IRYyPXiqdfGg1lpbbl+3A9NhwnnsIiiEPmmqJOW1uoSNjWX01jholvWbjGFg2vqqCt3sm91DWXOom4IFCZy19p66sqd3LW+bknFPhfrjrW11JU72b+6BrdDjnsh5lPUZ4fVonhoa7PRYczomwxyadTPltYKGspvX9itEK1tKGNtgznqrkTiCd66OoXbaWV3hyS1uXZ+2MfQdIjdq6qpcM1f3TsXtrVVsq0te93HaxvKi2rZ/stjfnong+xsq5K6Y2JJijppMZNEUvPdEwPEEpqeiQCfvmO10SEVvcNXJzlybQqAqhK7FLHLIU8wyg9PD6F1qiJyNsaRCGOEogm+f3KIpNaMesP86t4Oo0MSeaSou4dy6Xb94BYFLrsVgBKHNRchFbXFjEtwp/eDUrJPcs1utWBPF9Assd/+vc/mrJ5il+n31mpRM2U7XIvYt0LcKGctLUqp/6i1/j9ztT2z0FrzvZODXBsPcue6WvasmnuZbqUUH9vbzsBUiNV1pTmOsrhcHvPzw1NDVLkdPLanbd4Pzt0d1VSWOHA7rDRX3r5yr8icUqeNj+9tZ9wfZU39/OdDMpk6v3omgty1vk668TLszKCXF8+O0FTh4tHdrRmpxO6wWXhiXzvD3jBdddJ6KZYmK0mLUuqvZ98EfFIpVQagtf4X2diuGQWiCa6MpWYGvDPonTdpAahw2aloNrbvvhicH/YRS2jGfBFGvGFW1c79paiUMs34mmJUW+aktsy54GP80ThXx989vyRpyayzQ14SSc2AJ8RkIEpDRWbG2lW5HVS5ZSyLWLpsdQ99BKgBjgBH0z9j6d+PZmmbplTqsLKpuQKn3cLO9iqjwxHA1pZKSp1WWqtLpAUlz5U7bWxsKsdlt7JLzq+M29FeSYnDSld96W0TSCFyIVvdQ5uB/wA8BPyB1npQKfUnWuv/naXtmZZSioe2NhkdhrhBR62bp+5eY3QYIgOUUrxvm3lmABaaYpvVJMwvK0mL1toH/Eul1G7gy0qp55FBv0IIIYRYgWwPxL0DeBR4EvhllrdlCp5glFFfhNV1pTOzH4RxkknNlXE/VW4HddK8XTS84RhDnjCddW6ctuKdoTIdijHiDdNZWzozY0esTOcXnzc6hKKW7aSlETgMHAf+TimldAHPTQzHEjxzuI9wLMGGpnLeL83Whnvl0jjHeqawWRS/frCTSrcMdC508USSZw/3EogkWF1Xyod3FecaL7H0+xCMJrJWM0mIXMtq6q21/mNgPfC3wKeBi0qp/6iUWnBAgVLqr5RSryil/ms248u0eFITSdcNkcrR5hBM74cb940obAmtCceSAASixXseJpLvvg/BqBz7ojBkfZ0WrbVWSg0Dw0AcqAa+oZR6QWv9h7Mfnx4HU6a1vksp9d+VUnu11m9lO85MKHPaeHhbM31TwdtOvRz0hLg2EWBLc6Vc/WfR3evrcTms1Jc5F5yuGY0nOdHnoaLExsamihxGKJaiZyLAoCfMtrbKeWtTOW1WHtnezLWJANvbqnIboIm47FYe2dFMz0SAHct4H84MeglE4+xsr5KubmEaWU1alFK/C/w6MA78T+Bfaa1jSikLcBG4JWkBDgAvpH9/ETgI5EXSArCusZx1jQuPto8lknz7+ADReJJr40Ge3C/LWGdLqdPGvRsabvu4169McKwntWR/mdNGW7U726GJJfJH4nz3xCCJpGbEG16w26ervkzKLgBr6stYs4z3oWciwI/fGQYgFk9yaG1dpkMTYlmy3dJSAzyqte658UatdVIp9cg8z6kCrqR/nwa2zH6AUuop4CmAjo78+8JXgCVdKdZmyXzFWLF01/eDUmCzyFWlGVlU6l+C1FLwIntufH/lvRZmktWkRWv9Jwvcd3aeu6aB6+3zFYBnjuc+DTwN0N3dnZOBveP+CBP+KGsbylZ8EtusFj7W3UbfVIh1suLqssQTSS6PBagvd1KTgSqxB7pqqSyxU+Gy01RZnBW2zc7tsPF4dzvD02E2NGVm7RB/JE7fZJDO2tKc1pfyhmMMekJ01paasv5OW7Wbj+xqJRCNs0m6S4WJmLHK8+vA54GvAQ8Af29oNIAvHOPZw73EEprtbZXcv6lxxa+5mCXKxfx+em6UM4NeHDYLn7mjE7djZYey1aLY2lqZoehEtjRWuGjM0FLyAF99qw9vKEZTpYsn9uWu1farh/vwR+K017h5bE9bzra7FJ1SA02YkOnawbXWx4CwUuoVIKG1Pmx0TLGEJpZINejIKHxzCKX3QyyRnNk3QiyF1ppwLHUc5fK81jCz3VARz24SYjnM2NKC1vp3jY7hRjWlDh7a2sSIN8yeVflTkC0ST9A3GaS5soTSeWZa5Kv7NjVwrGeKlqoSKkvyZ/bVoCdEUuuCHOg76gsTjCTy5gpdKcUHd7RwadTP5pbcdYEo4IM7W7gyFpDWvWUY9YUJRRPzFjoVha2wvsmyaFNzBZua86tv9/snh+idDFJRYuezd3SiVOEMqKtw2blnEbOCzOTKmJ/vnhgE4JHtzbedZZZPRn1hnnmzj6TW3L2+bsFq5mbSXuOmvSb3CeSq2lL50l2GUW+YZw5fP87q8+oiUmSG6bqHROb4wjEgtcBaIildKEbzhd/tCvCGC6tbIBhJkEwvdl1o/zdhHv5IfOY4u/75JoqLtLQUsPdta+Zkn4e1DWXYZHEow21trcQfiaM17GgrrG6BzrpS7lpXhy8c58DqWqPDEQVqdV0pd66rwx+Jc6BLjrNiJElLAWuscPHeLU1GhyHSrBbFHQW8SFd3Z350CYn8pZRirxxnRU0uv/OU1NExl3giKV1wi5RMaqLxpNFhiCxIJDXxhOxbkT3S0pKHfvzOMGcGvWxqruChrdKSYrRBT4hvHx/AalF8rLs9I4vdFapwLMGzh3uZDsV575bGvBvcLuY3HYzx1SO9RONJPrSz1ZABzqLwSUtLHro44rvppzDW1fEA0XiSUDQ1xVzMb8wXYSoYI6k1l0b9RocjMqjfEyQQSRBLaK6MB4wORxQoSVry0IGuWspdNvbLQDRT2NJSQX25k5YqF+sapSzDQporXaxpKKPKbWdXR5XR4YgMWlNfRlt1CXVlDrbmcN0bUVykeygPdXfWyKBHE6lyO/jEgVVGh5EXbFYLH9zRYnQYIgtcdiuPd7cbHYYocJK0FIgj1yY52jPF5pYK7lpXb3Q4Rc8TjPK9k4MopfjQzhYqXPmzam+mRONJvndyEE8wykNbmwpyFWAxv+lQjO+dHASt+eCOVirdxXcOiMyT7qECcaRnimA0wdGeKZIyi8Vw54d9TPijjPsiXBwpzrEbA54QfZNBfOE4p/qnjQ5H5NjFER/jvgjj/igXRmX8ncgMaWkpEJuaKzjWM8XGpnIslsJZrj9fra4v5VivB4uCztribGFornRRU+rAG4qxvqlwShaIxemsK+VIz1TqdylZYBqdX3zetNu89qcP3/YxkrQUiPesr+fOtXVYJWExhYZyF5+/uwulKKiaT0vhslv51KFOkkktiXQRqitz8vm7u4DiPQdE5knSUkAkYTEX+aJOkfeheEmyIjJNxrQIIYQQIi9I0iKEEEKIvCDdQyZ3emCaM4NedrRXsUEGMxrOF47xs3OjOG0W7t/UiF2qZ6+Y1ppXLo4z6otw97o6GipcRodUsC6N+jnWM8WGpnJ2tFcZHY4QSyafuCamteZn50YZ8IT42blRo8MRwPFeD1fGApwd8hXtVOZMG/FGONozRd9kkNevTBgdTkF7+Xzq8+Sl86NS2FDkJUlaTEwpRXNl6qqzpSp7V59nh7z8z1eu8MKZEbSWNV4W0lLlQilw2Cw0VDizso1QNMFX3+rlf792jXF/JCvbWKxgNM6zh3v5h9evMZGlWCpL7JQ5U42+LVUlWdmGSLn+/jZWuLCZvJUwF8eeyD/SPWRyj+5uYyoYpcadvcrBR3qm8IXjnB6Y5kBXDeVFuHrrYq1tKOczd7iwWxVuR3ZOn8tjfgY9YQDODHq5e71xKxxfGQswNJ2OZcibldWWSxxWPnlwFcFoQipkZ9lDW5rYt7qGqhLzn+O5OPZE/jF3qi2wWhR1Zc6sThvdmB4r017jpjRLX8SFpLLEnrWEBaC92k2Z04bDZqGr3thFudqr3ZQ6relYslcM0mW3SsKSA5b054nZW1kgd8eeyC/yDSXY21nDrvaqvPggKwaVbjufu3M1GuPX3ql02/mNO7tMEYsoLnLsibnIt1SRmQxE+fmFMXongjfdvtyEJRiN8+qlcS6OSG2R5dBac7RnijevTNw0MNJiUcv+oD4z6OW1S+OEY4mZ295KF9RczpiluWI50efhtcvjROMymFMsrGciwM8vjDEViC75uSs5D+ajteZ47xSHr07KYOQ8JC0tReaHp4cY9UZ4u8/DU+/pwmmzLvq5wWicE30emitLWF2X6rZ4+fwY54d9KAW/XpadgamF7Nywj19cGANSV5PdnTVLev7ZIS/eUIxdHdU4bBaGpkP8+J1hAEKxBPdvaiQYTfDLi+MAuOwWtrRUkkxqTvZ7ANjRVrWk7sfLY35eSs9m0xruWFu3pJhF8YjEE3zvxCDxpGZgKsST+zuyti1PMMqZQS+ddaULDui+OOrn5fNjWYtDZJckLUXGlU5S7DYL1iUusf3i2VEuj/qxKMWn7+ikssSOI91CY1WZvyIqBi67dc7fF6N/KsiPTqcSlEg8yd3r67FbLViUIqk1Dltq39y4m53p284MeWc+uK0Wxfa2qmXGLI21Yn4WpbDbLMSjiawfK8+fSl2QHe/z8Pm7u+ZtPXbccPv180HkD0laiszD25u5POantapkyV1C9nRSYlHv9jHfs6Ge5ioXdWVOKvNgRoLZrK4r5dHdrcSTmjVLHGxos1hQKtXaYUvvj7oyJ493tzEdirGhMTXAusRu5QM7WrBa1EwLmc2qbnqdpWitKuGxPW2EYwnWNsgASTE/u9XCx/e2M+AJLfn4Xqrr54DVohasedSZPuei8aQcv3lIkpYi47Jb2dJSuazn3r+pkdbqEhrKXTPratislmW/nkhZVbu8GUJNlS4e3dWGLxJjY1PFzO0tVSW3NI/P/nDe2FQx09K2rnHpKy2317iXEbEoRlVuB1VZXLLhug/saOHiiJ/2GvdtW32Xe84J40nSIuaktWZoOky120GJI9Ud4LBZltSNIDJrOhQjEk/QUP7uQoMdtctPHuZKVqYCURJaUyfjk4RBJvwRlFJLngLvdtikNEERkKRFzOml86Oc7Jum3GXjkwdXLWnArsi8MV+EZw/3Ek9qHtzcyNbWzLdu9U8F+ebRATSaR7a3SNO5yLkrY36+d3IQheLR3a3SoiduIaOQxJzGfanpib5wnHBMpgUazROMEk+mpitna2n/yUCUpNZojSybLgwxEYiiNSS1ZmIZU6RF4ZOWFjGnezbU88bVSdqrS2SArQmsqS9j96pqgpE4e5c4LXqxNjVXMOaLEE9qaWYXhtjeVslUIIpFKTY3V9z+CaLoqHwvkKeUGgN6MviSdcB4Bl+v2La7GziWwdcrlPfFyO3KPjHXdjO9PyC3702hbSsb+yNbjDoHMmkx/4dVWus5i03lfdKSaUqpI1rrbtmuORTb+2L2/QHF997IPimObeWDQng/Vvp/kDEtQgghhMgLkrQIIYQQIi9I0nKrp2W7plJs74vZ9wcU33sj+6Q4tpUPCuH9WNH/Qca0CCGEECIvSEuLEEIIIfKCJC1CCCGEyAuStAghhBAiL8iKuEIIIYQJKaX2AAeBKsADvKG1PmJkTMullNoKbAUua63fWvbryEBcIYQQwlyUUn8FOIEXgWmgAngAiGutf9fI2BZLKfUjrfVDSql/CdwPPA/cAfRrrf9oOa9Z1N1DSql70j+rlFJ/oZT6kVLq/1FKNWd5u1uVUt9RSr2klHo5/fM7SqntWd7uv0z/3KGU+oVS6udKqVeVUndlc7tLYcQ+kf0xPzlHinuf5HI/5MN7n2N7tNa/pbX+ltb6p1rrb2utfxvYZXRgS+BI//wI8CGt9f/QWn8SWP4+1VoX7T/gZ+mfXwE+DrhIZbI/zvJ2XwGaZ93WArySo//vT4C16d/rgFeN3hdG7hPZH+baH7JPzLNPcrkf8uG9z/F+/kvg/wMeA96b/vnfgf9idGxL+D8MA/8A9AMlN9x+ZLmvWdQtLYBSSllInZTPaq3DWusXSX0IZH3bc/w9+7ZMq1FK3QfUaK0vAWitxwEz9REatU9kf8xNzhHZJ7naD/nw3ueM1vr3gP8B1AN7gAbgaa31vzQyriXaD/wbUl1CcQClVFn6tmWRgbjwUyCplKrSWnuUUuWAO8vb/E3gb5RSVbzbRTcBfCHL2/02qWa552b9f09nebtLlet9IvtjYXKOFO8+yeV+yJf3Pme01seB40bHsVxa6545bvMDP1zua8pAXCGEEELkhWLvHpqTUuozBm13WaOpM7Dd/2rEdpfCiH0i+2N+co6YTy73SS73Qz689yJ3pHtobseyvQE1x/x74G9zsN0tQEJrfe6Gm5/J9nYzIKv7RPbHksk5Yj5Z2Se53A95/N6bhlKqAjgDfEdr/c+NjifTpHtoDkopp9Y6ksXXN2T+vVLqL4BGIEZqVP5ntdZjSqmfaa3vy9Z2MyGb+0T2x9LJOWI+2dgnudwP+fzem0m6ZaoemCzEpKWoW1qUUk8Av0/qJPku8J91Kov7IZDNk2SP1vruWbd9Wyn18yxuE2Dv9e2m11n4ulLqD7K8zSUxaJ/I/piHnCNFv09yuR9M/96bhVLq35NKSv5L+u8vAaPAL0klfj8Cug0LMIuKOmkBfgc4oLWOK6V+E/iOUupTZH9a5RGl1P8HvAB4SV293E/2R4lblVIOrXVUa/22UuojwD8BW7K83aUwYp/I/pifnCPFvU9yuR/y4b03i78DvgX8l/T0948D+4BvAp8g1RpWkIq6e0gp9brW+uANf+8H/m+gQWu9Icvb3gUcINVPPA28np7els1t7gOuaa1Hb7jNCjyutX42m9teLKP2ieyPuck5IvskV/shH957M1FKvQD8IamWld8AXgbcWus/U0p9GuguxO6hYk9a/hmpVSR7b7itFfg3WuvfNC6y4iX7xFxkf5iP7BMBoJT6VeAQ0AT8b+DXSK1zkwTKSC2h//9qrb9oWJBZUNRJy2xKqa9orZ80Og7xLtkn5iL7w3xknxQnpZQDOAXYgXVa68QN932aAm1pKfYxLbNltQicWBbZJ+Yi+8N8ZJ8UIa11VCn1EuC5MWEpdJK03EyancxH9om5yP4wH9knRSg9APcA8Pjs+7TWfw/8fY5DyglZEVcIIYTII0qpzcAl4Kda64tGx5NL0tJys2xP4xRLJ/vEXGR/mI/skyKjtT4DdBkdhxFkIO4NlFKNWusRo+MQ75J9Yi6yP8xH9okoJpK0CCGEECIvyJgWIYQQQuQFSVqEEEIIkRckaRFiHkqpHymlPEqp7xsdS7FTSu1USr2ulHpHKfV2ejVQYRCl1Cql1DGl1In0PpGVeEVOyJgWIeahlLofcAOf11o/YnQ8xUwptR7QWuuLSqkW4CiwSWvtMTay4pRejVVprSNKqTLgNHBIaz1ocGiiwElLi4GUUnvTV40upVRp+oplq9FxFRul1L9XSv3LG/7+klLqd7XWPwV8xkVWnObaH8D7rq9Hkf5iHAXqjYmwuMyzP76gtY6kb3Ii3yUiR6SlxWBKqf8LcAElQL/W+j8ZHFLRUUp1At/SWu9OrzJ5EdintZ5QSt0D/IG0tOTOQvsjff8+UgXitmitk8ZFWhzm2x+kWiGfB9YC/0pr/f8YF6UoFrK4nPH+PfAWEAb+hcGxFCWt9TWl1IRSahepMu/Hr39BitxbaH8opZqBfwQ+JQlLbiywPyaA7enuuu8opb4h68WIbJOkxXi1pMqI20m1uASMDado/U/g06TKvP+dsaEI5tgfSqkKUlf2/1pr/YZxoRWlec8PrfWgUuo0cBfwjdyHJoqJdA8ZTCn1PeBZYDXQXIilxPPBfGXepXvIGLP3B2AFfgg8p7X+LwaGVpTm2B/NwITWOqSUqgbeBD6qtT5lYJiiCEhLi4GUUr8OxLTWX1FKWYHXlFL3aa1/ZnRsxWauMu9KqVeAjUCZUqof+JzW+sdGxlksZu8PpdQTwN1ArVLq0+mHfVprfcKoGIvJHPtjE/AXSilNqvbR/y0Ji8gFaWkRgpky78eAx4utaqoZyf4wF9kfwixkmpooesVc5t2MZH+Yi+wPYSbS0iKEEEKIvCAtLUIIIYTIC5K0CCGEECIvSNIihBBCiLwgSYsQQggh8oIkLUIIIYTIC/9/ZDTBqI+b1JoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x576 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(xy,figsize=(9,8)); #矩阵散点图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面计算实际数据（通常是定量数据）的线性相关系数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>支出</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>女</td>\n",
       "      <td>167</td>\n",
       "      <td>71</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>男</td>\n",
       "      <td>171</td>\n",
       "      <td>68</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>女</td>\n",
       "      <td>175</td>\n",
       "      <td>73</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>男</td>\n",
       "      <td>169</td>\n",
       "      <td>74</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>男</td>\n",
       "      <td>154</td>\n",
       "      <td>55</td>\n",
       "      <td>25.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>男</td>\n",
       "      <td>175</td>\n",
       "      <td>68</td>\n",
       "      <td>44.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>女</td>\n",
       "      <td>166</td>\n",
       "      <td>65</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>女</td>\n",
       "      <td>159</td>\n",
       "      <td>58</td>\n",
       "      <td>71.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>女</td>\n",
       "      <td>169</td>\n",
       "      <td>73</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>女</td>\n",
       "      <td>165</td>\n",
       "      <td>67</td>\n",
       "      <td>56.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   性别   身高  体重    支出\n",
       "0   女  167  71  46.0\n",
       "1   男  171  68  10.4\n",
       "2   女  175  73  21.0\n",
       "3   男  169  74   4.9\n",
       "4   男  154  55  25.9\n",
       ".. ..  ...  ..   ...\n",
       "47  男  175  68  44.4\n",
       "48  女  166  65   5.3\n",
       "49  女  159  58  71.4\n",
       "50  女  169  73   5.5\n",
       "51  女  165  67  56.8\n",
       "\n",
       "[52 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd           #读取BSdata分析用数据\n",
    "BS=pd.read_excel('DaPy_data.xlsx','BSdata')[['性别','身高','体重','支出']];BS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9118170987010521"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4sqRhSXdKmg8sbDqF/kVgYasnS1onaUjS0OjoaDFRd5m7FM2sCvIk8LnASuD2iFgBvMKE6ZJorEVsuR4xIjZFxEBEDPT19c023kpwl6KZVUGeBL4X2BsRW7L799NI6PslLQLIPr9UTojV4y5FM6uCaRN4RLwI7JE0np0uBH4APAxclY1dBTxUSoQVtGZFPzevXUb/gnkI6F8wz9usmlnH5W2l/zTwz5JOAJ4BPkYj+d8n6WrgeeDyckKspjUr+p2wzayrciXwiNgOHNPGSePduJmZdYE7Mc3MEuUEbmaWKG8nW7KUW+5Tjt2sDpzASzTxYODxlnug8okw5djN6sJTKCVKueU+5djN6sIJvEQpt9ynHLtZXTiBlyjllvuUYzerCyfwEqXccp9y7GZ14SJmiVI+GDjl2M3qwocam5lVnA81NjPrMU7gZmaJcgI3M0tUrYqYbg03s15SmwTu1nAz6zW1mUJxa7iZ9ZraJHC3hptZr6lNAndruJn1mlwJXNJzknZI2i5pKBu7SdJINrZd0iVlBDg4PMLqjY+zZMO3WL3xcQaHR2b0fdwabma9pp0i5vsj4qcTxr4YEbcWGVCzIguPbg03s15T6VUoUxUeZ5J4fZK8mfWSvHPgATwqaaukdU3j10h6UtLdkk5p9URJ6yQNSRoaHR1tKzgXHs3MJpc3gb8nIlYCFwOfknQ+cDtwNrAc2Afc1uqJEbEpIgYiYqCvr6+t4Fx4NDObXK4EHhEj2eeXgAeBVRGxPyLGIuJV4A5gVdHBufBoZja5aefAJc0HjouIX2a3Pwj8laRFEbEve9hlwM6ig2un8Og2eTOrmzxFzIXAg5LGH//1iPiOpK9KWk5jfvw54ONlBJin8Og2eTOro2kTeEQ8A5zTYvyjpUQ0A0WvVjEzS0FPdGJ6tYqZ1VFPJHCvVjGzOuqJBO7VKmZWR5XuxMzLbfJmVkc9kcDBbfJmVj89MYViZlZHTuBmZolyAjczS5QTuJlZopzAzcwS5QRuZpYoJ3Azs0Q5gZuZJcoJ3MwsUU7gZmaJcgI3M0uUE7iZWaKcwM3MEpVrN0JJzwG/BMaA30bEgKRTgXuBxTTOxLw8In5eTpjT86HGZlY37bwDf39ELI+Igez+BuCxiHgz8Fh2vyvGDzUeOXCI4MihxoPDI90KycysdLOZQrkUuCe7fQ+wZtbRzNBUhxqbmfWqvAk8gEclbZW0LhtbGBH7stsvAgtbPVHSOklDkoZGR0dnGW5rPtTYzOoobwJ/T0SsBC4GPiXp/OYvRkTQSPLHiIhNETEQEQN9fX2zi3YSPtTYzOooVwKPiJHs80vAg8AqYL+kRQDZ55fKCnI6PtTYzOpo2gQuab6k3xm/DXwQ2Ak8DFyVPewq4KGygpzOmhX93Lx2Gf0L5iGgf8E8bl67zKtQzKyn5VlGuBB4UNL4478eEd+R9H/AfZKuBp4HLi8vzOn5UGMzq5tpE3hEPAOc02L8ZeDCMoIyM7PpuRPTzCxRTuBmZolyAjczS5QTuJlZotTowenQi0mjNFaslOl04Kclv0ZZUo4d0o4/5dgh7fhTjh06E/8bI+KYTsiOJvBOkDTUtOFWUlKOHdKOP+XYIe34U44duhu/p1DMzBLlBG5mlqheTOCbuh3ALKQcO6Qdf8qxQ9rxpxw7dDH+npsDNzOri158B25mVgtO4GZmiUoqgUu6W9JLknY2jd0kaUTS9uzjkmx8saRDTeP/2L3IX4v1mPiz8U9L+pGkpyT9bdP49ZKelrRL0kWdj/ioGHPHnsq1l3RvU4zPSdre9LVKX/vJYk/o2i+X9EQW45CkVdm4JH0pu/ZPSlrZvchfi7Wd+N8n6WDT9f98qcFFRDIfwPnASmBn09hNwF+0eOzi5sdV4WOS+N8P/AdwYnb/d7PPbwO+D5wILAF+AsxJJPYkrv2Er98GfD6Vaz9F7Elce+BR4OLs9iXAd5tufxsQcB6wJbH43wf8a6diS+odeET8N/CzbscxU5PE/0lgY0T8OnvM+MlGlwL/EhG/johngadpnITUFW3GXjlT/dtRY7P7y4HN2VAK1x5oGXvlTBJ/AK/Pbp8MvJDdvhT4p2h4AlgwfvJXt7QZf0cllcCncE3259bdkk5pGl8iaVjSf0l6b9eim9pbgPdK2pLF+c5svB/Y0/S4vdlYlUwWO6Rx7ce9F9gfET/O7qdw7cdNjB3SuPbXArdI2gPcClyfjady7a+ldfwA75L0fUnflvQHZQbRCwn8duBsYDmwj8afk2S3z4qIFcBnga9Len3L79Bdc4FTafy5uJ7GKUfqbki5TRZ7Ktd+3JVU+B3sNCbGnsq1/yRwXUScCVwH3NXleNo1WfzbaOxbcg7w98BgmUEkn8AjYn9EjEXEq8AdZH/qZn/+vpzd3kpjHvMt3Yt0UnuBB7I/Gb8HvEpjc5wR4Mymx70hG6uSlrEndO2RNBdYC9zbNJzCtW8Ze0LX/irggez2NzgyRZXEtWeS+CPiFxHxq+z2vwHHSzq9rCCST+AT5scuo3HgMpL6JM3Jbr8JeDPwTOcjnNYgjWIgkt4CnEBjZ7OHgSsknShpCY34v9etICcxSIvYE7r2AB8AfhQRe5vGUrj20CL2hK79C8AfZrcvAMangB4G/ixbjXIecDAi9nUjwGm0jF/S743/BZ2tTDkOeLm0KLpd4W3ng8afivuAwzTe/V0NfBXYATxJ45e/KHvsh4GngO00/qz544rGfwLwNRr/8WwDLmh6/F/SeAe1i6zinULsqVz7bPwrwCdaPL7S136y2FO59sB7gK00VvtsAd6RPVbAP2TXfgcwkFj812TX//vAE8C7y4zNrfRmZolKfgrFzKyunMDNzBLlBG5mligncDOzRDmBm5klygnczCxRTuBmZon6f+AXRndIsPlnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(BS['身高'],BS['体重'],'o');\n",
    "BS['身高'].corr(BS['体重'])   #BS.身高.corr(BS.体重)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>身高</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.911817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>体重</th>\n",
       "      <td>0.911817</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          身高        体重\n",
       "身高  1.000000  0.911817\n",
       "体重  0.911817  1.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BS[['身高','体重']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>支出</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>身高</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.911817</td>\n",
       "      <td>0.043881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>体重</th>\n",
       "      <td>0.911817</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.042946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>支出</th>\n",
       "      <td>0.043881</td>\n",
       "      <td>0.042946</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          身高        体重        支出\n",
       "身高  1.000000  0.911817  0.043881\n",
       "体重  0.911817  1.000000  0.042946\n",
       "支出  0.043881  0.042946  1.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BS.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif']=['SimSun'];  #设置中文字体为'宋体'\n",
    "pd.plotting.scatter_matrix(BS,figsize=(9,8)); #矩阵散点图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7.1.3 样本相关系数的检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 0.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as st #加载统计分析方法包 \n",
    "st.pearsonr(x,y1)        #pearson相关及检验 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0, 0.0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.pearsonr(x,y2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.907917278678589, 3.2236304802661585e-08)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.pearsonr(x,y3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.9140292045707707, 1.7775582357873907e-08)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.pearsonr(x,y4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9118170987010525, 5.7473293164451325e-21)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(BS['身高'],BS['体重'],'o');\n",
    "st.pearsonr(BS.身高,BS.体重)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.043881130759588965, 0.7574067077940487)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(BS['身高'],BS.支出,'o');\n",
    "st.pearsonr(BS.身高,BS.支出)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.2 简单线性回归分析模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 　7.2.1  直线回归模型的建立"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7.2.1.1 直线回归模型的概念"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7.2.1.2 直线回归的可视化模拟"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义模拟直线回归函数\n",
    "import statsmodels.api as sm                  #加载统计模型包\n",
    "def reglinedemo(n=20):                        #模拟样本例数\n",
    "    x=np.arange(n)+1                          #自变量取值\n",
    "    e=np.random.normal(0,1,n)                 #误差项\n",
    "    y=2+0.5*x+e                               #因变量值\n",
    "    x1=sm.add_constant(x);x1                  #加常数项\n",
    "    fm=sm.OLS(y,x1).fit();fm                  #模型拟合，见下\n",
    "    plt.plot(x,y,'.',x,fm.fittedvalues,'r-'); #添加回归线，红色\n",
    "    for i in range(len(x)):                   #画垂直线\n",
    "        plt.vlines(x,y,fm.fittedvalues,linestyles='dotted',colors='b');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(12)\n",
    "reglinedemo(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(12)\n",
    "reglinedemo(40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7.2.1.3 最小二乘估计方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（1）散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=BS.体重; x=BS.身高 \n",
    "plt.plot(x,y,'o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（2）模型拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const   -79.282827\n",
       "身高        0.876949\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm            #加载线性回归模型库\n",
    "fm1=sm.OLS(y,sm.add_constant(x)).fit()  #最小二乘估计，加常数项\n",
    "fm1.params                              #模型参数的估计值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（3）回归直线拟合图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "yfit=fm1.fittedvalues #拟合估计值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y,'.',x,yfit, 'r-'); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 　7.2.2  直线回归模型的检验"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7.2.2.1 回归系数的检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    -8.414938\n",
       "身高       15.702810\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm1.tvalues      #模型系数t值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    3.820690e-11\n",
       "身高       5.747329e-21\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm1.pvalues      #系数检验p值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>-79.282827</td>\n",
       "      <td>-8.414938</td>\n",
       "      <td>3.820690e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>身高</th>\n",
       "      <td>0.876949</td>\n",
       "      <td>15.702810</td>\n",
       "      <td>5.747329e-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               b          t             p\n",
       "const -79.282827  -8.414938  3.820690e-11\n",
       "身高      0.876949  15.702810  5.747329e-21"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pd.DataFrame({'b':fm1.params,'t':fm1.tvalues,'p':fm1.pvalues})  #格式输出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>支出</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>女</td>\n",
       "      <td>167</td>\n",
       "      <td>71</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>男</td>\n",
       "      <td>171</td>\n",
       "      <td>68</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>女</td>\n",
       "      <td>175</td>\n",
       "      <td>73</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>男</td>\n",
       "      <td>169</td>\n",
       "      <td>74</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>男</td>\n",
       "      <td>154</td>\n",
       "      <td>55</td>\n",
       "      <td>25.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>男</td>\n",
       "      <td>175</td>\n",
       "      <td>68</td>\n",
       "      <td>44.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>女</td>\n",
       "      <td>166</td>\n",
       "      <td>65</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>女</td>\n",
       "      <td>159</td>\n",
       "      <td>58</td>\n",
       "      <td>71.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>女</td>\n",
       "      <td>169</td>\n",
       "      <td>73</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>女</td>\n",
       "      <td>165</td>\n",
       "      <td>67</td>\n",
       "      <td>56.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   性别   身高  体重    支出\n",
       "0   女  167  71  46.0\n",
       "1   男  171  68  10.4\n",
       "2   女  175  73  21.0\n",
       "3   男  169  74   4.9\n",
       "4   男  154  55  25.9\n",
       ".. ..  ...  ..   ...\n",
       "47  男  175  68  44.4\n",
       "48  女  166  65   5.3\n",
       "49  女  159  58  71.4\n",
       "50  女  169  73   5.5\n",
       "51  女  165  67  56.8\n",
       "\n",
       "[52 rows x 4 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd           #读取BSdata分析用数据\n",
    "BS=pd.read_excel('DaPy_data.xlsx','BSdata')[['性别','身高','体重','支出']];BS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  -79.2828</td> <td>    9.422</td> <td>   -8.415</td> <td> 0.000</td> <td>  -98.207</td> <td>  -60.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>身高</th>        <td>    0.8769</td> <td>    0.056</td> <td>   15.703</td> <td> 0.000</td> <td>    0.765</td> <td>    0.989</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf  #加载公式模型建立包\n",
    "fm2=smf.ols('体重~身高',data=BS).fit()   \n",
    "fm2.summary().tables[1]    #回归系数检验表，来自summary的第2张表   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>体重</td>        <th>  R-squared:         </th> <td>   0.831</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.828</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   246.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 07 Dec 2021</td> <th>  Prob (F-statistic):</th> <td>5.75e-21</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>01:08:05</td>     <th>  Log-Likelihood:    </th> <td> -133.21</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    52</td>      <th>  AIC:               </th> <td>   270.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    50</td>      <th>  BIC:               </th> <td>   274.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm2.summary().tables[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.599</td> <th>  Durbin-Watson:     </th> <td>   2.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.037</td> <th>  Jarque-Bera (JB):  </th> <td>   5.704</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.782</td> <th>  Prob(JB):          </th> <td>  0.0577</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.434</td> <th>  Cond. No.          </th> <td>3.58e+03</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm2.summary().tables[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  -79.2878</td> <td>    9.518</td> <td>   -8.331</td> <td> 0.000</td> <td>  -98.414</td> <td>  -60.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>身高</th>        <td>    0.8768</td> <td>    0.056</td> <td>   15.528</td> <td> 0.000</td> <td>    0.763</td> <td>    0.990</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>支出</th>        <td>    0.0011</td> <td>    0.021</td> <td>    0.050</td> <td> 0.960</td> <td>   -0.041</td> <td>    0.044</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm3=smf.ols('体重~身高+支出',data=BS).fit()\n",
    "fm3.summary().tables[1]    #回归系数检验表，来自summary的第2张表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7.2.2.2 回归模型的检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>体重</td>        <th>  R-squared:         </th> <td>   0.831</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.825</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   120.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 07 Dec 2021</td> <th>  Prob (F-statistic):</th> <td>1.14e-19</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>01:08:10</td>     <th>  Log-Likelihood:    </th> <td> -133.21</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    52</td>      <th>  AIC:               </th> <td>   272.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    49</td>      <th>  BIC:               </th> <td>   278.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  -79.2878</td> <td>    9.518</td> <td>   -8.331</td> <td> 0.000</td> <td>  -98.414</td> <td>  -60.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>身高</th>        <td>    0.8768</td> <td>    0.056</td> <td>   15.528</td> <td> 0.000</td> <td>    0.763</td> <td>    0.990</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>支出</th>        <td>    0.0011</td> <td>    0.021</td> <td>    0.050</td> <td> 0.960</td> <td>   -0.041</td> <td>    0.044</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.641</td> <th>  Durbin-Watson:     </th> <td>   2.165</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.036</td> <th>  Jarque-Bera (JB):  </th> <td>   5.744</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.784</td> <th>  Prob(JB):          </th> <td>  0.0566</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.440</td> <th>  Cond. No.          </th> <td>3.62e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.62e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     体重   R-squared:                       0.831\n",
       "Model:                            OLS   Adj. R-squared:                  0.825\n",
       "Method:                 Least Squares   F-statistic:                     120.8\n",
       "Date:                Tue, 07 Dec 2021   Prob (F-statistic):           1.14e-19\n",
       "Time:                        01:08:10   Log-Likelihood:                -133.21\n",
       "No. Observations:                  52   AIC:                             272.4\n",
       "Df Residuals:                      49   BIC:                             278.3\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    -79.2878      9.518     -8.331      0.000     -98.414     -60.161\n",
       "身高             0.8768      0.056     15.528      0.000       0.763       0.990\n",
       "支出             0.0011      0.021      0.050      0.960      -0.041       0.044\n",
       "==============================================================================\n",
       "Omnibus:                        6.641   Durbin-Watson:                   2.165\n",
       "Prob(Omnibus):                  0.036   Jarque-Bera (JB):                5.744\n",
       "Skew:                          -0.784   Prob(JB):                       0.0566\n",
       "Kurtosis:                       3.440   Cond. No.                     3.62e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.62e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run init.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 　7.2.3  直线回归模型的预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    69.7986\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm2.predict(pd.DataFrame({'身高':[170]}))         #估计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    87.3375\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm2.predict(pd.DataFrame({'身高':[190]}))          #预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    69.7986\n",
       "1    78.5681\n",
       "2    87.3375\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm2.predict(pd.DataFrame({'身高': [170,180,190]}))  #估计与预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.3 分组可视化模型分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>171</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>169</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>154</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>183</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>178</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>158</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>169</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>166</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>175</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     身高  体重\n",
       "1   171  68\n",
       "3   169  74\n",
       "4   154  55\n",
       "5   183  76\n",
       "9   173  63\n",
       "..  ...  ..\n",
       "37  178  78\n",
       "40  158  60\n",
       "45  169  65\n",
       "46  166  70\n",
       "47  175  68\n",
       "\n",
       "[27 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BS_M=BS[BS.性别=='男'][['身高','体重']];BS_M "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>167</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>175</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>169</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>166</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>165</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>164</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>166</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>159</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>169</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>165</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     身高  体重\n",
       "0   167  71\n",
       "2   175  73\n",
       "6   169  71\n",
       "7   166  66\n",
       "8   165  69\n",
       "..  ...  ..\n",
       "44  164  62\n",
       "48  166  65\n",
       "49  159  58\n",
       "50  169  73\n",
       "51  165  67\n",
       "\n",
       "[25 rows x 2 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BS_F=BS[BS.性别=='女'][['身高','体重']];BS_F "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 　7.3.1 可视化分组线性相关分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（1）男生身高与体重的相关分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9105200024864898, 4.43657339837424e-11)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as st \n",
    "M_r=st.pearsonr(BS_M.身高,BS_M.体重);M_r "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings  \n",
    "warnings.filterwarnings(\"ignore\")            #忽略警告信息\n",
    "\n",
    "import seaborn as sns \n",
    "sns.jointplot('身高','体重',data=BS_M);  #直方图+散点图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（2）女生身高与体重的相关分析 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8931328371576219, 1.907324154689927e-09)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plt.plot(BS_F.身高,BS_F.体重,'o');\n",
    "#plt.title(M_r);\n",
    "st.pearsonr(BS_F.身高,BS_F.体重)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot('身高','体重',data=BS_F);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 　7.3.2 可视化分组线性回归模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（1）男生身高与体重的回归分析 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  -85.4332</td> <td>   14.189</td> <td>   -6.021</td> <td> 0.000</td> <td> -114.657</td> <td>  -56.210</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>身高</th>        <td>    0.9101</td> <td>    0.083</td> <td>   11.011</td> <td> 0.000</td> <td>    0.740</td> <td>    1.080</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smf.ols('体重~身高',data=BS_M).fit().summary().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(y=\"体重\",x=\"身高\",data=BS_M,ci=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot('身高','体重',data=BS_M,kind='reg');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（2）女生身高与体重的回归分析 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  -80.3893</td> <td>   15.405</td> <td>   -5.218</td> <td> 0.000</td> <td> -112.257</td> <td>  -48.522</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>身高</th>        <td>    0.8867</td> <td>    0.093</td> <td>    9.523</td> <td> 0.000</td> <td>    0.694</td> <td>    1.079</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smf.ols('体重~身高',data=BS_F).fit().summary().tables[1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(y='体重',x='身高',data=BS_F,ci=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot('身高','体重',data=BS_F,kind='reg',ci=0); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（3）基于plotnine的分组回归分析可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotnine import *    \n",
    "theme_set(theme_bw(base_family='SimSun'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (14373846)>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(BS,aes('身高','体重')) + geom_point() + facet_wrap('性别',nrow=1)\n",
    "        + stat_smooth(method='lm',se=True))   #无可信区间的回归线 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (17556679)>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(BS,aes('身高','体重')) + geom_point() + facet_wrap('性别',nrow=2) \n",
    "        + stat_smooth(method='lm',se=False))           #有可信区间的回归线 "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
